Subject: comp.graphics.algorithms Frequently Asked Questions
From: [email-masked] (Joseph O'Rourke)
Date: 15 Dec 99 06:11:08 GMT
Newsgroups: comp.graphics.algorithms,news.answers,comp.answers

Posted-By: auto-faq 3.2.1.4
Archive-name: graphics/algorithms-faq
Posting-Frequency: bi-weekly

Welcome to the FAQ for comp.graphics.algorithms!

Thanks to all who have contributed.  Corrections and contributions
(to [email-masked]) always welcome.  

----------------------------------------------------------------------
This article is Copyright 1999 by Joseph O'Rourke.  It may be freely 
redistributed in its entirety provided that this copyright notice is 
not removed.
----------------------------------------------------------------------

  Changed items this posting (|): 0.04, 0.07, 1.06, 3.01, 5.09, 7.02
  New     items this posting (+): none

----------------------------------------------------------------------
History of Changes (approx. last six months):
----------------------------------------------------------------------
Changes in 15 Dec 99 posting:
  0.04: Updated [Farin] to 4th ed. (Thanks to Matt Aschenbaum).
  0.07: Updated link to Phillippe's nurbs library.
        (Thanks to Andrzej Serafin.)
	Fixed bspfaq link (Thanks to Juhana Sadeharju).
  1.06: Added  Northwoods to commercial graph layout software.
  3.01: Added another method for rotating a bitmap.
        (Thanks to Rudi Rasmussen.)
  5.09: Notification of stale link on ray-tracing caustics.
        (Thanks to Vladimir Lebedev-Schmidthof.)
Changes in  1 Oct 99 posting:
  5.06: Add link to full Mo:ller-Trumbore paper.
Changes in 15 Sep 99 posting:
  0.02: Archives now at www.faqs.org; OSU no more.
  0.03: Ditto.
  2.01: Correct typo in polygon area formula.
Changes in 1  Sep 99 posting:
  0.04: Updated [Russ] and added [Russ & Russ]
  1.03: Fixed error re collinear segment intersection
        (thanks to Amitha Perera). 
  2.01: Added more efficient polygon area formula (thanks to
        Olexandr Frantchuk).
  5.23: New (short) article on surface reconstruction.
[Earlier changes list available upon request from FAQ maintainer.]

----------------------------------------------------------------------
Table of Contents
----------------------------------------------------------------------

0. General Information
   0.01: Charter of comp.graphics.algorithms
   0.02: Are the postings to comp.graphics.algorithms archived?
   0.03: How can I get this FAQ?
|  0.04: What are some must-have books on graphics algorithms?
   0.05: Are there any online references?
   0.06: Are there other graphics related FAQs?
|  0.07: Where is all the source?

1. 2D Computations: Points, Segments, Circles, Etc.
   1.01: How do I rotate a 2D point?
   1.02: How do I find the distance from a point to a line?
   1.03: How do I find intersections of 2 2D line segments?
   1.04: How do I generate a circle through three points?
   1.05: How can the smallest circle enclosing a set of points be found?
|  1.06: Where can I find graph layout algorithms?

2. 2D Polygon Computations
   2.01: How do I find the area of a polygon?
   2.02: How can the centroid of a polygon be computed?
   2.03: How do I find if a point lies within a polygon?
   2.04: How do I find the intersection of two convex polygons?
   2.05: How do I do a hidden surface test (backface culling) with 2d points?
   2.06: How do I find a single point inside a simple polygon?
   2.07: How do I find the orientation of a simple polygon?

3. 2D Image/Pixel Computations
|  3.01: How do I rotate a bitmap?
   3.02: How do I display a 24 bit image in 8 bits?
   3.03: How do I fill the area of an arbitrary shape?
   3.04: How do I find the 'edges' in a bitmap?
   3.05: How do I enlarge/sharpen/fuzz a bitmap?
   3.06: How do I map a texture on to a shape?
   3.07: How do I detect a 'corner' in a collection of points?
   3.08: Where do I get source to display (raster font format)?
   3.09: What is morphing/how is it done?
   3.10: How do I quickly draw a filled triangle?
   3.11: D Noise functions and turbulence in Solid texturing.
   3.12: How do I generate realistic sythetic textures?
   3.13: How do I convert between color models (RGB, HLS, CMYK, CIE etc)?
   3.14: How is "GIF" pronounced?

4. Curve Computations
   4.01: How do I generate a Bezier curve that is parallel to another Bezier?
   4.02: How do I split a Bezier at a specific value for t?
   4.03: How do I find a t value at a specific point on a Bezier?
   4.04: How do I fit a Bezier curve to a circle?

5. 3D computations
   5.01: How do I rotate a 3D point?
   5.02: What is ARCBALL and where is the source?
   5.03: How do I clip a polygon against a rectangle?
   5.04: How do I clip a polygon against another polygon?
   5.05: How do I find the intersection of a line and a plane?
   5.06: How do I determine the intersection between a ray and a triangle?
   5.07: How do I determine the intersection between a ray and a sphere?
   5.08: How do I find the intersection of a ray and a Bezier surface?
|  5.09: How do I ray trace caustics?
   5.10: What is the marching cubes algorithm?
   5.11: What is the status of the patent on the "marching cubes" algorithm?
   5.12: How do I do a hidden surface test (backface culling) with 3d points?
   5.13: Where can I find algorithms for 3D collision detection?
   5.14: How do I perform basic viewing in 3d?
   5.15: How do I optimize/simplify a 3D polygon mesh?
   5.16: How can I perform volume rendering?
   5.17: Where can I get the spline description of the famous teapot etc.?
   5.18: How can the distance between two lines in space be computed?
   5.19: How can I compute the volume of a polyhedron?
   5.20: How can I decompose a polyhedron into convex pieces?
   5.21: How can the circumsphere of a tetrahedron be computed?
   5.22: How do I determine if two triangles in 3D intersect?
   5.23: How can a 3D surface be reconstructed from a collection of points?

6. Geometric Structures and Mathematics
   6.01: Where can I get source for Voronoi/Delaunay triangulation?
   6.02: Where do I get source for convex hull?
   6.03: Where do I get source for halfspace intersection?
   6.04: What are barycentric coordinates?
   6.05: How do I generate a random point inside a triangle?
   6.06: How do I evenly distribute N points on (tesselate) a sphere?
   6.07: What are coordinates for the vertices of an icosohedron?
   6.08: How do I generate random points on the surface of a sphere?
   6.09: What are Plucker coordinates?

7. Contributors
   7.01: How can you contribute to this FAQ?
|  7.02: Contributors.  Who made this all possible.

Search e.g. for "Section 6" to find that section.
Search e.g. for "Subject 6.04" to find that item.
----------------------------------------------------------------------
Section 0. General Information
----------------------------------------------------------------------
Subject 0.01: Charter of comp.graphics.algorithms

    comp.graphics.algorithms is an unmoderated newsgroup intended as a forum
    for the discussion of the algorithms used in the process of generating
    computer graphics.  These algorithms may be recently proposed in
    published journals or papers, old or previously known algorithms, or
    hacks used incidental to the process of computer graphics.  The scope of
    these algorithms may range from an efficient way to multiply matrices,
    all the way to a global illumination method incorporating raytracing,
    radiosity, infinite spectrum modeling, and perhaps even mirrored balls
    and lime jello.

    It is hoped that this group will serve as a forum for programmers and
    researchers to exchange ideas and ask questions on recent papers or
    current research related to computer graphics.

    comp.graphics.algorithms is not:

     - for requests for gifs, or other pictures
     - for requests for image translator or processing software; see
            alt.binaries.pictures* FAQ  
            alt.binaries.pictures.utilities [now degenerated to pic postings]
            alt.graphics.pixutils (image format translation)
            comp.sources.misc (image viewing source code)
            sci.image.processing
            comp.graphics.apps.softimage
            fj.comp.image
     - for requests for compression software; for these try:
            alt.comp.compression
            comp.compression
            comp.compression.research

----------------------------------------------------------------------
Subject 0.02: Are the postings to comp.graphics.algorithms archived?

    Archives may be found at: http://www.faqs.org/

----------------------------------------------------------------------
Subject 0.03: How can I get this FAQ?

    The FAQ is posted on the 1st and 15th of every month.  The easiest
    way to get it is to search back in your news reader for the most
    recent posting, with Subject: 
          comp.graphics.algorithms Frequently Asked Questions
    It is posted to comp.graphics.algorithms, and cross-posted to
    news.answers and comp.answers.  

    If you can't find it on your newsreader,
    you can look at the latest HTML version at either of these two sites:
      http://www.exaflop.org/docs/cgafaq
      http://www.faqs.org/
    Both versions should be up-to-date and nicely converted.
    The maintainer also keeps a copy of the raw ASCII, accessible via 
      http://cs.smith.edu/~orourke/.

    Finally, you can ftp the FAQ from several sites, including:

      ftp://rtfm.mit.edu/pub/faqs/graphics/algorithms-faq
      ftp://ftp.seas.gwu.edu/pub/rtfm/comp/graphics/algorithms/comp.graphics.algorithms_Frequently_Asked_Questions

    The (busy) rtfm.mit.edu site lists many alternative "mirror" sites.
    Also can reach the FAQ from http://www.geom.umn.edu/software/cglist/,
    which is worth visiting in its own right.

----------------------------------------------------------------------
|Subject 0.04: What are some must-have books on graphics algorithms?

    The keywords in brackets are used to refer to the books in later
    questions.  They generally refer to the first author except where
    it is necessary to resolve ambiguity or in the case of the Gems.

    Basic computer graphics, rendering algorithms,
    ----------------------------------------------

    [Foley]
    Computer Graphics: Principles and Practice (2nd Ed.),
    J.D. Foley, A. van Dam, S.K. Feiner, J.F. Hughes, Addison-Wesley
    1990, ISBN 0-201-12110-7;
    Computer Graphics: Principles and Practice, C version
    J.D. Foley,  A. van Dam, S.K. Feiner, J.F. Hughes, Addison-Wesley 
    ISBN: 0-201-84840-6, 1996, 1147 pp.

    [Rogers:Procedural]
    Procedural Elements for Computer Graphics, Second Edition
    David F. Rogers, WCB/McGraw Hill 1998, ISBN 0-07-053548-5

    [Rogers:Mathematical]
    Mathematical Elements for Computer Graphics 2nd Ed.,
    David F. Rogers and J. Alan Adams, McGraw Hill 1990, ISBN
    0-07-053530-2

    [Watt:3D]
    _3D Computer Graphics, 2nd Edition_,
    Alan Watt, Addison-Wesley 1993, ISBN 0-201-63186-5

    [Glassner:RayTracing]
    An Introduction to Ray Tracing,
    Andrew Glassner (ed.), Academic Press 1989, ISBN 0-12-286160-4

    [Gems I]
    Graphics Gems,
    Andrew Glassner (ed.), Academic Press 1990, ISBN 0-12-286165-5

    [Gems II]
    Graphics Gems II,
    James Arvo (ed.), Academic Press 1991, ISBN 0-12-64480-0

    [Gems III]
    Graphics Gems III,
    David Kirk (ed.), Academic Press 1992, ISBN 0-12-409670-0 (with
    IBM disk) or 0-12-409671-9 (with Mac disk)
    See also "AP Professional Graphics CD-ROM Library,"
    Academic Press,  ISBN 0-12-059756-X, which contains Gems I-III.

    [Gems IV]
    Graphics Gems IV,
    Paul S. Heckbert (ed.), Academic Press 1994, ISBN 0-12-336155-9
    (with IBM disk) or 0-12-336156-7 (with Mac disk)

    [Gems V]
    Graphic Gems V,
    Alan W. Paeth (ed.), Academic Press 1995, ISBN 0-12-543455-3
    (with IBM disk)

    [Watt:Animation]
    Advanced Animation and Rendering Techniques,
    Alan Watt, Mark Watt, Addison-Wesley 1992, ISBN 0-201-54412-1

    [Bartels]
    An Introduction to Splines for Use in Computer Graphics and
        Geometric Modeling,
    Richard H. Bartels, John C. Beatty, Brian A. Barsky, 1987, ISBN
    0-934613-27-3

    [Farin]
    Curves and Surfaces for Computer Aided Geometric Design:
|   A Practical Guide, 4th Edition, Gerald E. Farin, Academic Press
|   1996. ISBN 0122490541.

    [Prusinkiewicz]
    The Algorithmic Beauty of Plants,
    Przemyslaw W. Prusinkiewicz, Aristid Lindenmayer, Springer-Verlag,
    1990, ISBN 0-387-97297-8, ISBN 3-540-97297-8

    [Oliver]
    Tricks of the Graphics Gurus,
    Dick Oliver, et al. (2) 3.5 PC disks included, $39.95 SAMS Publishing

    [Hearn]
    Introduction to computer graphics,
    Hearn & Baker

    [Cohen]
    Radiosity and Realistic Imange Sythesis,
    Michael F. Cohen, John R. Wallace, Academic Press Professional
    1993, ISBN 0-12-178270-0 [apparently now out of print]

    [Ashdown]
    Radiosity: A Programmer's Perspective
    Ian Ashdown, John Wiley & Sons 1994, ISBN 0-471-30444-1, 498 pp.

    [Sillion]
    Radiosity & Global Illumination
    Francois X. Sillion snd Claude Puech, Morgan Kaufmann 1994, ISBN
    1-55860-277-1, 252 pp.

    [Ebert]
    Texturing and Modeling - A Procedural Approach (2nd Ed.)
    David S. Ebert (ed.), F. Kenton Musgrave, Darwyn Peachey, Ken Perlin,
    Steven Worley, Academic Press 1998, ISBN 0-12-228730-4, Includes CD-ROM.

    [Schroeder]
    Visualization Toolkit, 2nd Edition, The: An Object-Oriented Approach to
    3-D Graphics (Bk/CD) (Professional Description)
    William J. Schroeder,  Kenneth Martin, and Bill Lorensen,
    Prentice-Hall 1998, ISBN: 0-13-954694-4
    See Subject 0.07 for source.

    [Anderson]
    PC Graphics Unleashed
    Scott Anderson. SAMS Publishing, ISBN 0-672-30570-4

    [Ammeraal]
    Computer Graphics for Java Programmers,
    Leen Ammeraal, John Wiley 1998, ISBN 0-471-98142-7.
    Additional information at http://home.wxs.nl/~ammeraal/ .

    For image processing,
    ---------------------

    [Barnsley]
    Fractal Image Compression,
    Michael F. Barnsley and Lyman P. Hurd, AK Peters, Ltd, 1993 ISBN
    1-56881-000-8

    [Jain]
    Fundamentals of Image Processing,
    Anil K. Jain, Prentice-Hall 1989, ISBN 0-13-336165-9

    [Castleman]
    Digital Image Processing,
    Kenneth R. Castleman, Prentice-Hall 1996, ISBN(Cloth): 0-13-211467-4
    (Description and errata at: "http://www.phoenix.net/~castlman")

    [Pratt]
    Digital Image Processing, Second Edition,
    William K. Pratt, Wiley-Interscience 1991, ISBN 0-471-85766-1

    [Gonzalez]
    Digital Image Processing (3rd Ed.),
    Rafael C. Gonzalez, Paul Wintz, Addison-Wesley 1992, ISBN
    0-201-50803-6

    [Russ]
    The Image Processing Handbook (3rd Ed.),
    John C. Russ, CRC Press and IEEE Press 1998, ISBN 0-8493-2532-3
    [Russ & Russ]
    The Image Processing Tool Kit v. 3.0
    Chris Russ and John Russ, Reindeer Games Inc. 1999, ISBN 1-928808-00-X

    [Wolberg]
    Digital Image Warping,
    George Wolberg, IEEE Computer Society Press Monograph 1990, ISBN
    0-8186-8944-7

    Computational geometry
    ----------------------

    [Bowyer]
    A Programmer's Geometry,
    Adrian Bowyer, John Woodwark, Butterworths 1983, 
    ISBN 0-408-01242-0 Pbk

    [O'Rourke (C)]
    Computational Geometry in C (2nd Ed.)
    Joseph O'Rourke, Cambridge University Press 1998, 
    ISBN 0-521-64010-5 Pbk, ISBN 0-521-64976-5 Hbk
    Additional information and code at http://cs.smith.edu/~orourke/ .

    [O'Rourke (A)]
    Art Gallery Theorems and Algorithms
    Joseph O'Rourke, Oxford University Press 1987,
    ISBN 0-19-503965-3.

    [Goodman & O'Rourke]
    Handbook of Discrete and Computational Geometry
    J. E. Goodman and J. O'Rourke, editors.
    CRC Press LLC, July 1997.
    ISBN:0-8493-8524-5
    Additional information at http://cs.smith.edu/~orourke/ .

    [Samet:Application]
    Applications of Spatial Data Structures:  Computer Graphics, 
    Image Processing, and GIS, 
    Hanan Samet, Addison-Wesley, Reading, MA, 1990.
    ISBN 0-201-50300-0.

    [Samet:Design & Analysis]
    The Design and Analysis of Spatial Data Structures,
    Hanan Samet, Addison-Wesley, Reading, MA, 1990.
    ISBN 0-201-50255-0.

    [Mortenson]
    Geometric Modeling,
    Michael E. Mortenson, Wiley 1985, ISBN 0-471-88279-8

    [Preparata]
    Computational Geometry: An Introduction,
    Franco P. Preparata, Michael Ian Shamos, Springer-Verlag 1985,
    ISBN 0-387-96131-3

    [Okabe]
    Spatial Tessellations: Concepts and Applications of Voronoi Diagrams,
    A. Okabe and B. Boots and K. Sugihara,
    John Wiley, Chichester, England, 1992.

    [Overmars]
    Computational Geometry: Algorithms and Applications
    M. de Berg and M. van Kreveld and M. Overmars and O. Schwarzkopf
    Springer-Verlag, Berlin, 1997.

    [Stolfi]
    Oriented Projective Geometry: A Framework for Geometric Computations
    Academic Press, 1991.

    [Hodge]
    Methods of Algebraic Geometry, Volume 1
    W.V.D. Hodge and D. Pedoe, Cambridge, 1994.
    ISBN 0-521-469007-4 Paperback

    Algorithms books with chapters on computational geometry
    --------------------------------------------------------

    [Cormen et al.]
    Introduction to Algorithms,
    T. H. Cormen, C. E. Leiserson, R. L. Rivest,
    The MIT Press, McGraw-Hill, 1990.

    [Mehlhorn]
    Data Structures and Algorithms,
    K. Mehlhorn,
    Springer-Verlag, 1984.

    [Sedgewick]
    R. Sedgewick,
    Algorithms,
    Addison-Wesley, 1988.

    Solid Modelling
    ---------------

    [Mantyla]
    Introduction to Solid Modeling
    Martti Mantyla, Computer Science Press 1988,
    ISBN 07167-8015-1

----------------------------------------------------------------------
Subject 0.05: Are there any online references?

    The computational geometry community maintains its own
    bibliography of publications in or closely related to that
    subject.  Every four months, additions and corrections are
    solicited from users, after which the database is updated and
    released anew.  As of 31 Mar 1998, it contained 11,275 bib-tex
    entries.  See Jeff Erickson's page on "Computational Geometry
    Bibliographies": 
      http://www.cs.duke.edu/~jeffe/compgeom/biblios.html#geombib
    The bibliography can be retrieved from:

    ftp://ftp.cs.usask.ca/pub/geometry/geombib.tar.gz - bibliography proper
    ftp://ftp.cs.usask.ca/pub/geometry/o-cgc19.ps.gz  - overview published
        in '93 in SIGACT News and the Internat. J. Comput.  Geom. Appl.
    ftp://ftp.cs.usask.ca/pub/geometry/ftp-hints      - detailed retrieval info

    Universitat Politecnica de Catalunya maintains a search engine at:
       http://www-ma2.upc.es/~geomc/geombibe.html

    The ACM SIGGRAPH Online Bibliography Project, by
    Stephen Spencer ([email-masked]).
    The database is available for anonymous FTP from the
    ftp://siggraph.org/publications/bibliography directory.  Please
    download and examine the file READ_ME in that directory for more
    specific information concerning the database.

    'netlib' is a useful source for algorithms, member inquiries for
    SIAM, and bibliographic searches.  For information, send mail to
    [email-masked], with "send index" in the body of the mail
    message.

    You can also find free sources for numerical computation in C via
    ftp://usc.edu/pub/C-numanal.  In particular, grab
    numcomp-free-c.gz in that directory.

    Check out Nick Fotis's computer graphics resources FAQ -- it's
    packed with pointers to all sorts of great computer graphics
    stuff.  This FAQ is posted biweekly to comp.graphics.

    This WWW page contains links to a large number
    of computer graphic related pages:
    http://www.dataspace.com:84/vlib/comp-graphics.html

    There's a Computer Science Bibliography Server at:
    http://glimpse.cs.arizona.edu:1994/bib/
    with Computer Graphics, Vision and Radiosity sections

    A comprehensive bibliography of color quantization papers and articles 
    (CQUANT97) is available at http://www.ledalite.com/library-/cgis.htm.

    Modelling physically based systems for animation:
    http://www.cc.gatech.edu/gvu/animation/Animation.html

    The University of Manchester NURBS Library:
    ftp://unix.hensa.ac.uk/pub/misc/unix/nurbs/

    For an implementation of Seidel's algorithm for fast trapezoidation
    and triangulation of polygons. You can get the code from:
    ftp://ftp.cs.unc.edu/pub/users/narkhede/triangulation.tar.gz

    Ray tracing bibliography:
    ftp://ftp.eye.com/pub/graphics/papers/rtbib95.tar.Z
    ftp://ftp.eye.com/pub/graphics/papers/rtbib95.zip

    Quaternions and other comp sci curiosities:
    ftp://ftp.netcom.com/pub/hb/hbaker/hakmem/hakmem.html

    Directory of Computational Geometry Software,
    collected by Nina Amenta ([email-masked])
    Nina Amenta is maintaining a WWW directory to computational 
    geometry software. The directory lives at The Geometry Center. 
    It has pointers to lots of convex hull and voronoi diagram programs, 
    triangulations, collision detection, polygon intersection, smallest 
    enclosing ball of a point set and other stuff.
    http://www.geom.umn.edu/software/cglist/

    A compact reference for real-time 3d computer graphics programming:
    http://www.math.mcgill.ca/~loisel/

    RADBIB99 is a comprehensive bibliography of radiosity and
    related global illumination papers, theses, articles, and
    books. It currently includes 1,593 references.
    This bibliography is available in BibTex format as
    RADBIB99.BIB (with a release date of June 1, 1999) from:
      http://www.helios32.com/radbib99.bib

    The "Electronic Visualization Library" (EVlib) is a domain-
    secific digital library for Scientific Visualization and 
    Computer Graphics:  http://visinfo.zib.de/

----------------------------------------------------------------------
Subject 0.06: Are there other graphics related FAQs?

    BSP Tree FAQ by Bretton Wade
        http://reality.sgi.com/bspfaq/

    Gamma and Color FAQs by Charles A. Poynton has
        ftp://ftp.inforamp.net/pub/users/poynton/doc/colour/
        http://www.inforamp.net/~poynton/

    The documents are mirrored in Darmstadt, Germany at
        ftp://ftp.igd.fhg.de/pub/doc/colour/

----------------------------------------------------------------------
|Subject 0.07: Where is all the source?

    Graphics Gems source code.
    http://www.acm.org/tog/GraphicsGems/
    This site is now the offical distribution site for Graphics Gems code.

    Master list of Computational Geometry software:
    http://www.geom.umn.edu/software/cglist
    Described in [Goodman & O'Rourke], Chap. 52.

    Jeff Erikson's software list:
    http://compgeom.cs.uiuc.edu/~jeffe/compgeom/compgeom.html

    General 'stuff'
    ftp://wuarchive.wustl.edu/graphics/graphics

    There are a number of interesting items in
    http://graphics.lcs.mit.edu/~seth including:
    - Code for 2D Voronoi, Delaunay, and Convex hull
    - Mike Hoymeyer's implementation of Raimund Seidel's
      O( d! n ) time linear programming algorithm for
      n constraints in d dimensions
    - geometric models of UC Berkeley's new computer science
      building

    Sources to "Computational Geometry in C", by J. O'Rourke
    can be found at ftp://cs.smith.edu/pub/compgeom

    Greg Ferrar has uploaded his heavily commented C++ 3D rendering
    library at ftp://ftp.math.ohio-state.edu/pub/users/gregt

    TAGL is a portable and extensible library that provides a subset
    of Open-GL functionalities.
    ftp://sunsite.unc.edu/pub/packages/programming/graphics/tagl21.tgz

    Try ftp://x2ftp.oulu.fi  for /pub/msdos/programming/docs/graphpro.lzh by
    Michael Abrash. His XSharp package has an implementation of Xiaoulin
    Wu's anti-aliasing algorithm (in C).

    Example sources for BSP tree algorithms can be found at
|   http://reality.sgi.com/bspfaq/, item 24.

    Mel Slater ([email-masked]) also made some implementations of
    BSP trees and shadows for static scenes using shadow volumes
    code available
    http://www.dcs.qmw.ac.uk/~mel/BSP.html
    ftp://ftp.dcs.qmw.ac.uk/people/mel/BSP

    The Visualization Toolkit (A visualization textbook, C++ library 
    and Tcl-based interpreter) (see [Schroeder]): 
    http://www.kitware.com/vtk.html

    WINGED.ZIP, a C++ implementation of Baumgart's winged-edge data structure:
    http://www.ledalite.com/library-/cgis.htm

    CGAL, the Computational Geometry Algorithms Library, is written in C++ 
    and is available at URL http://www.cs.ruu.nl/CGAL/ .  It consists of 
    three parts. The first part is the kernel, which consists of constant 
    size non-modifiable geometric primitive objects.  The second part is 
    a collection of basic geometric datastructures and algorithms, which 
    are parameterized by traits classes that define the interface between 
    the datastructure or algorithm, and the primitives they use. 
    The third part consists of non-geometric support facilities.

    A C++ NURBS library written by Lavoie Philippe. Version 2.1. 
    Results may be exported as POV-Ray, RIB (renderman) or VRML files.
    It also offers wrappers to OpenGL:
|      http://yukon.genie.uottawa.ca/~lavoie/software/nurbs/

    Paul Bourke has code for several problems, including isosurface 
    generation and Delauney triangulation, at:
       http://www.mhri.edu.au/~pdb/geometry/
       http://www.mhri.edu.au/~pdb/modeling/

    A nearly comprehensive list of available 3D engines 
    (most with source code):
       http://cg.cs.tu-berlin.de/~ki/engines.html

    See also 5.17: 
        Where can I get the spline description of the famous teapot etc.?

    Interactive Geometry Software called "Cinderella":
        http://www.cinderella.de

----------------------------------------------------------------------
Section 1. 2D Computations: Points, Segments, Circles, Etc.
----------------------------------------------------------------------
Subject 1.01: How do I rotate a 2D point?

    In 2-D, the 2x2 matrix is very simple.  If you want to rotate a
    column vector v by t degrees using matrix M, use

        M = {{cos t, -sin t}, {sin t, cos t}} in M*v.

    If you have a row vector, use the transpose of M (turn rows into
    columns and vice versa).  If you want to combine rotations, in 2-D
    you can just add their angles, but in higher dimensions you must
    multiply their matrices.

----------------------------------------------------------------------
Subject 1.02: How do I find the distance from a point to a line?

    Let the point be C (Cx,Cy) and the line be AB (Ax,Ay) to (Bx,By).
    Let P be the point of perpendicular projection of C on AB.  The parameter
    r, which indicates P's position along AB, is computed by the dot product 
    of AC and AB divided by the square of the length of AB:
    
    (1)     AC dot AB
        r = ---------  
            ||AB||^2
    
    r has the following meaning:
    
        r=0      P = A
        r=1      P = B
        r<0      P is on the backward extension of AB
        r>1      P is on the forward extension of AB
        0<r<1    P is interior to AB
    
    The length of a line segment in d dimensions, AB is computed by:
    
        L = sqrt( (Bx-Ax)^2 + (By-Ay)^2 + ... + (Bd-Ad)^2)

    so in 2D:   
    
        L = sqrt( (Bx-Ax)^2 + (By-Ay)^2 )
    
    and the dot product of two vectors in d dimensions, U dot V is computed:
    
        D = (Ux * Vx) + (Uy * Vy) + ... + (Ud * Vd)
    
    so in 2D:   
    
        D = (Ux * Vx) + (Uy * Vy) 
    
    So (1) expands to:
    
            (Cx-Ax)(Bx-Ax) + (Cy-Ay)(By-Ay)
        r = -------------------------------
                          L^2

    The point P can then be found:

        Px = Ax + r(Bx-Ax)
        Py = Ay + r(By-Ay)

    And the distance from A to P = r*L.

    Use another parameter s to indicate the location along PC, with the 
    following meaning:
           s<0      C is left of AB
           s>0      C is right of AB
           s=0      C is on AB

    Compute s as follows:

            (Ay-Cy)(Bx-Ax)-(Ax-Cx)(By-Ay)
        s = -----------------------------
                        L^2

    Then the distance from C to P = s*L.

----------------------------------------------------------------------
Subject 1.03: How do I find intersections of 2 2D line segments?

    This problem can be extremely easy or extremely difficult depends
    on your applications.  If all you want is the intersection point,
    the following should work:

    Let A,B,C,D be 2-space position vectors.  Then the directed line
    segments AB & CD are given by:

        AB=A+r(B-A), r in [0,1]
        CD=C+s(D-C), s in [0,1]

    If AB & CD intersect, then

        A+r(B-A)=C+s(D-C), or

        Ax+r(Bx-Ax)=Cx+s(Dx-Cx)
        Ay+r(By-Ay)=Cy+s(Dy-Cy)  for some r,s in [0,1]

    Solving the above for r and s yields

            (Ay-Cy)(Dx-Cx)-(Ax-Cx)(Dy-Cy)
        r = -----------------------------  (eqn 1)
            (Bx-Ax)(Dy-Cy)-(By-Ay)(Dx-Cx)

            (Ay-Cy)(Bx-Ax)-(Ax-Cx)(By-Ay)
        s = -----------------------------  (eqn 2)
            (Bx-Ax)(Dy-Cy)-(By-Ay)(Dx-Cx)

    Let P be the position vector of the intersection point, then

        P=A+r(B-A) or

        Px=Ax+r(Bx-Ax)
        Py=Ay+r(By-Ay)

    By examining the values of r & s, you can also determine some
    other limiting conditions:

        If 0<=r<=1 & 0<=s<=1, intersection exists
            r<0 or r>1 or s<0 or s>1 line segments do not intersect

        If the denominator in eqn 1 is zero, AB & CD are parallel
        If the numerator in eqn 1 is also zero, AB & CD are collinear.

    If they are collinear, then the segments may be projected to the x- 
    or y-axis, and overlap of the projected intervals checked.

    If the intersection point of the 2 lines are needed (lines in this
    context mean infinite lines) regardless whether the two line
    segments intersect, then

        If r>1, P is located on extension of AB
        If r<0, P is located on extension of BA
        If s>1, P is located on extension of CD
        If s<0, P is located on extension of DC

    Also note that the denominators of eqn 1 & 2 are identical.

    References:

    [O'Rourke (C)] pp. 249-51
    [Gems III] pp. 199-202 "Faster Line Segment Intersection,"

----------------------------------------------------------------------
Subject 1.04: How do I generate a circle through three points?

    Let the three given points be a, b, c.  Use _0 and _1 to represent
    x and y coordinates. The coordinates of the center p=(p_0,p_1) of
    the circle determined by a, b, and c are:

        A = b_0 - a_0;
        B = b_1 - a_1;
        C = c_0 - a_0;
        D = c_1 - a_1;
    
        E = A*(a_0 + b_0) + B*(a_1 + b_1);
        F = C*(a_0 + c_0) + D*(a_1 + c_1);
    
        G = 2.0*(A*(c_1 - b_1)-B*(c_0 - b_0));
    
        p_0 = (D*E - B*F) / G;
        p_1 = (A*F - C*E) / G;
 
    If G is zero then the three points are collinear and no finite-radius
    circle through them exists.  Otherwise, the radius of the circle is:

            r^2 = (a_0 - p_0)^2 + (a_1 - p_1)^2

    Reference:

    [O' Rourke (C)] p. 201. Simplified by Jim Ward.

----------------------------------------------------------------------
Subject 1.05: How can the smallest circle enclosing a set of points be found?

    This circle is often called the minimum spanning circle.  It can be
    computed in O(n log n) time for n points.  The center lies on
    the furthest-point Voronoi diagram.  Computing the diagram constrains
    the search for the center.  Constructing the diagram can be accomplished
    by a 3D convex hull algorithm; for that connection, see, e.g.,
    [O'Rourke (C), p.195ff].  For direct algorithms, see:
      S. Skyum, "A simple algorithm for computing the smallest enclosing circle"
      Inform. Process. Lett. 37 (1991) 121--125.
      J. Rokne, "An Easy Bounding Circle" [Gems II] pp.14--16.

----------------------------------------------------------------------
|Subject 1.06: Where can I find graph layout algorithms?

    See the paper by Eades and Tamassia, "Algorithms for Drawing
    Graphs: An Annotated Bibliography," Tech Rep CS-89-09, Dept.  of
    CS, Brown Univ, Feb. 1989.

    A revised version of the annotated bibliography on graph drawing
    algorithms by Giuseppe Di Battista, Peter Eades, and Roberto
    Tamassia is now available from

    ftp://wilma.cs.brown.edu/pub/papers/compgeo/gdbiblio.tex.gz and
    ftp://wilma.cs.brown.edu/pub/papers/compgeo/gdbiblio.ps.gz

    Commercial software includes the Graph Layout Toolkit from Tom Sawyer
|   Software http://www.tomsawyer.com and Northwoods Software's GO++
|   http://www.nwoods.com/go/ .

----------------------------------------------------------------------
Section 2. 2D Polygon Computations
----------------------------------------------------------------------
Subject 2.01: How do I find the area of a polygon?

    The signed area can be computed in linear time by a simple sum.
    The key formula is this:

        If the coordinates of vertex v_i are x_i and y_i,
        twice the signed area of a polygon is given by

        2 A( P ) = sum_{i=0}^{n-1} (x_i y_{i+1} - y_i x_{i+1}).

    Here n is the number of vertices of the polygon. A rearrangement
    of terms in this equation can save multiplications and operate on
    coordinate differences, and so may be both faster and more
    accurate:

       2 A( P ) = sum_{i=0}^{n-1} ((x_i + x_{i+1}) (y_{i+1} - y_i)).

    References: [O' Rourke (C)] Thm. 1.3.3, p. 21; [Gems II] pp. 5-6:
    "The Area of a Simple Polygon", Jon Rokne.

    To find the area of a planar polygon not in the x-y plane, use:
    
       2 A(P) = abs(N . (sum_{i=0}^{n-1} (v_i x v_{i+1})))
    
       where N is a unit vector normal to the plane. The `.' represents the
       dot product operator, the `x' represents the cross product operator,
       and abs() is the absolute value function.
    
    [Gems II] pp. 170-171:
    "Area of Planar Polygons and Volume of Polyhedra", Ronald N. Goldman.

----------------------------------------------------------------------
Subject 2.02: How can the centroid of a polygon be computed?

    The centroid (a.k.a. the center of mass, or center of gravity)
    of a polygon can be computed as the weighted sum of the centroids
    of a partition of the polygon into triangles.  The centroid of a
    triangle is simply the average of its three vertices, i.e., it
    has coordinates (x1 + x2 + x3)/3 and (y1 + y2 + y3)/3.  This 
    suggests first triangulating the polygon, then forming a sum
    of the centroids of each triangle, weighted by the area of
    each triangle, the whole sum normalized by the total polygon area.
    This indeed works, but there is a simpler method:  the triangulation
    need not be a partition, but rather can use positively and
    negatively oriented triangles (with positive and negative areas),
    as is used when computing the area of a polygon.  This leads to
    a very simple algorithm for computing the centroid, based on a
    sum of triangle centroids weighted with their signed area.
    The triangles can be taken to be those formed by any fixed point,
    e.g., the vertex v0 of the polygon, and the two endpoints of 
    consecutive edges of the polygon: (v1,v2), (v2,v3), etc.  The area 
    of a triangle with vertices a, b, c is half of this expression:
                (b[X] - a[X]) * (c[Y] - a[Y]) -
                (c[X] - a[X]) * (b[Y] - a[Y]);
 
    Code available at ftp://cs.smith.edu/pub/code/centroid.c (3K).
    Reference: [Gems IV] pp.3-6; also includes code.

----------------------------------------------------------------------
Subject 2.03: How do I find if a point lies within a polygon?

    The definitive reference is "Point in Polyon Strategies" by
    Eric Haines [Gems IV]  pp. 24-46.
    The code in the Sedgewick book Algorithms (2nd Edition, p.354) is 
    incorrect.

    The essence of the ray-crossing method is as follows.
    Think of standing inside a field with a fence representing the polygon. 
    Then walk north. If you have to jump the fence you know you are now 
    outside the poly. If you have to cross again you know you are now 
    inside again; i.e., if you were inside the field to start with, the total 
    number of fence jumps you would make will be odd, whereas if you were 
    ouside the jumps will be even.

    The code below is from Wm. Randolph Franklin <[email-masked]>
    with some minor modifications for speed.  It returns 1 for strictly
    interior points, 0 for strictly exterior, and 0 or 1 for points on
    the boundary.  The boundary behavior is complex but determined;
    in particular, for a partition of a region into polygons, each point
    is "in" exactly one polygon.  See the references below for more detail.

    int pnpoly(int npol, float *xp, float *yp, float x, float y)
    {
      int i, j, c = 0;
      for (i = 0, j = npol-1; i < npol; j = i++) {
        if ((((yp[i]<=y) && (y<yp[j])) ||
             ((yp[j]<=y) && (y<yp[i]))) &&
            (x < (xp[j] - xp[i]) * (y - yp[i]) / (yp[j] - yp[i]) + xp[i]))

          c = !c;
      }
      return c;
    }

    The code may be further accelerated, at some loss in clarity, by
    avoiding the central computation when the inequality can be deduced,
    and by replacing the division by a multiplication for those processors
    with slow divides.  For code that distinguishes strictly interior
    points from those on the boundary, see [O'Rourke (C)] pp. 239-245.

    References:
    [Gems IV]  pp. 24-46
    [O'Rourke (C)]
    [Glassner:RayTracing]

----------------------------------------------------------------------
Subject 2.04: How do I find the intersection of two convex polygons?

    Unlike intersections of general polygons, which might produce a
    quadratic number of pieces, intersection of convex polygons of n
    and m vertices always produces a polygon of at most (n+m) vertices.
    There are a variety of algorithms whose time complexity is proportional
    to this size, i.e., linear.  The first, due to Shamos and Hoey, is
    perhaps the easiest to understand.  Let the two polygons be P and
    Q.  Draw a horizontal line through every vertex of each.  This
    partitions each into trapezoids (with at most two triangles, one
    at the top and one at the bottom).  Now scan down the two polygons
    in concert, one trapezoid at a time, and intersect the trapezoids
    if they overlap vertically.
       A more difficult-to-describe algorithm is in [O'Rourke (C)], pp. 242-252.
    This walks around the boundaries of P and Q in concert, intersecting
    edges.  An implementation is included in [O'Rourke (C)].

----------------------------------------------------------------------
Subject 2.05: How do I do a hidden surface test (backface culling) with 2d points?

    c = (x1-x2)*(y3-y2)-(y1-y2)*(x3-x2)

    x1,y1, x2,y2, x3,y3 = the first three points of the polygon.

    If c is positive the polygon is visible. If c is negative the
    polygon is invisible (or the other way).

----------------------------------------------------------------------
Subject 2.06: How do I find a single point inside a simple polygon?

   Given a simple polygon, find some point inside it.  Here is a method
   based on the proof that there exists an internal diagonal, in
   [O'Rourke (C), 13-14].  The idea is that the midpoint of a diagonal
   is interior to the polygon.
 
   1. Identify a convex vertex v; let its adjacent vertices be a and b.
   2. For each other vertex q do:
   	2a. If q is inside avb, compute distance to v (orthogonal to ab).
   	2b. Save point q if distance is a new min.
   3. If no point is inside, return midpoint of ab, or centroid of avb.
   4. Else if some point inside, qv is internal: return its midpoint.
 
   Code for finding a diagonal is in [O'Rourke (C), 35-39], and is part
   of many other software packages.  See Subject 0.07: Where is all the 
   source?

----------------------------------------------------------------------
Subject 2.07: How do I find the orientation of a simple polygon?

    Compute the signed area (Subject 2.01).  The orientation is 
    counter-clockwise if this area is positive.  

    A slightly faster method is based on the observation that it isn't
    necessary to compute the area.  Find the lowest vertex (or, if 
    there is more than one vertex with the same lowest coordinate, 
    the rightmost of those vertices) and then take the cross product 
    of the edges fore and aft of it.  Both methods are O(n) for n vertices, 
    but it does seem a waste to add up the total area when a single cross
    product (of just the right edges) suffices.  Code for this is
    available at ftp://cs.smith.edu/pub/code/polyorient.C (2K).

    The reason that the lowest, rightmost (or any other such extreme) point
    works is that the internal angle at this vertex is necessarily convex, 
    strictly less than pi (even if there are several equally-lowest points).

----------------------------------------------------------------------
Section 3. 2D Image/Pixel Computations
----------------------------------------------------------------------
|Subject 3.01: How do I rotate a bitmap?

    The easiest way, according to the comp.graphics faq, is to take
    the rotation transformation and invert it. Then you just iterate
    over the destination image, apply this inverse transformation and
    find which source pixel to copy there.

    A much nicer way comes from the observation that the rotation
    matrix:

        R(T) = { { cos(T), -sin(T) }, { sin(T), cos(T) } }

    is formed my multiplying three matrices, namely:

        R(T) = M1(T) * M2(T) * M3(T)

    where

        M1(T) = { { 1, -tan(T/2) },
                  { 0, 1         } }
        M2(T) = { { 1,      0    },
                  { sin(T), 1    } }
        M3(T) = { { 1, -tan(T/2) },
                  { 0,         1 } }

    Each transformation can be performed in a separate pass, and
    because these transformations are either row-preserving or
    column-preserving, anti-aliasing is quite easy.

|   Another fast approach is to perform first a column-preserving roation, 
|   and then a row-preserving rotation. For an image W pixels wide and 
|   H pixels high, this requires W+H BitBlt operations in comparison to 
|   the brute-force rotation, which uses W*H SetPixel operations (and a 
|   lot of multiplying).

    Reference:

    Paeth, A. W., "A Fast Algorithm for General Raster Rotation",
    Proceedings Graphics Interface '89, Canadian Information
    Processing Society, 1986, 77-81
    [Note - e-mail copies of this paper are no longer available]

    [Gems I]

----------------------------------------------------------------------
Subject 3.02: How do I display a 24 bit image in 8 bits?

    [Gems I] pp. 287-293, "A Simple Method for Color Quantization:
    Octree Quantization"

    B. Kurz.  Optimal Color Quantization for Color Displays.
    Proceedings of the IEEE Conference on Computer Vision and Pattern
    Recognition, 1983, pp. 217-224.

    [Gems II] pp. 116-125, "Efficient Inverse Color Map Computation"

        This describes an efficient technique to
        map actual colors to a reduced color map,
        selected by some other technique described
        in the other papers.

    [Gems II] pp. 126-133, "Efficient Statistical Computations for
    Optimal Color Quantization"

    Xiaolin Wu.  Color Quantization by Dynamic Programming and
    Principal Analysis.  ACM Transactions on Graphics, Vol. 11, No. 4,
    October 1992, pp 348-372.

----------------------------------------------------------------------
Subject 3.03: How do I fill the area of an arbitrary shape?

    "A Fast Algorithm for the Restoration of Images Based on Chain
        Codes Description and Its Applications", L.W. Chang & K.L. Leu,
        Computer Vision, Graphics, and Image Processing, vol.50,
        pp296-307 (1990)

    "An Introductory Course in Computer Graphics" by Richard Kingslake,
        (2nd edition) published by Chartwell-Bratt ISBN 0-86238-284-X

    [Gems I]
    [Foley]
    [Hearn]

----------------------------------------------------------------------
Subject 3.04: How do I find the 'edges' in a bitmap?

    A simple method is to put the bitmap through the filter:

        -1    -1    -1
        -1     8    -1
        -1    -1    -1

    This will highlight changes in contrast.  Then any part of the
    picture where the absolute filtered value is higher than some
    threshold is an "edge".

    A more appropriate edge detector for noisy images is
    described by Van Vliet et al. "A nonlinear Laplace operator
    as edge detector in noisy images", in Computer Vision,
    Graphics, and image processing 45, pp. 167-195, 1989.

----------------------------------------------------------------------
Subject 3.05: How do I enlarge/sharpen/fuzz a bitmap?

    Sharpening of bitmaps can be done by the following algorithm:

    I_enh(x,y) = I_fuz(x,y)-k*Laplace(I_fuz(x,y))

    or in words:  An image can be sharpened by subtracting a positive
    fraction k of the Laplace from the fuzzy image.

    The Laplace is the kernal:
        1   1   1
        1  -8   1
        1   1   1

    The following library implements Fast Gaussian Blurs:

    MAGIC: An Object-Oriented Library for Image Analysis by David Eberly

    The library source code and the documentation (in Latex) are  at
        http://www.magic-software.com/
    The code compiles on Unix systems using g++ and on PCs using
    Microsoft Windows 3.1 and Borland C++.  The fast Gaussian blurring
    is based on a finite difference method for solving s u_s = s^2 \nabla^2 u
    where s is the standard deviation of the Gaussian (t = s^2/2).  It
    takes advantage of geometrically increasing steps in s (rather than
    linearly increasing steps in t), thus getting to a larger "time" rapidly,
    but still retaining stability.  Section 4.5 of the  documentation contains
    the algorithm description and implementation.

     A bitmap is a sampled image, a special case of a digital signal,
     and suffers from two limitations common to all digital signals.
     First, it cannot provide details at fine enough spacing to exactly
     reproduce every continuous image, nor even more detailed sampled
     images. And second, each sample approximates the infinitely fine
     variability of ideal values with a discrete set of ranges encoded
     in a small number of bits---sometimes just one bit per pixel. Many
     times bitmaps have another limitation imposed: The values canot be
     negative. The resolution limitation is perhaps most important.

     The ideal way to enlarge a bitmap is to work from the original
     continuous image, magnifying and resampling it. The standard way
     to do it in practice is to (conceptually) reconstruct a continuous
     image from the bitmap, and magnify and resample that instead. This
     will not give the same results, since details of the original have
     already been lost, but it is the best approach possible given an
     already sampled image. More details are provided below.

     Both sharpening and fuzzing are examples of filtering. Even more
     specifically, they can be both be accomplished with filters which
     are linear and shift invariant. A crude way to sharpen along a row
     (or column) is to set output pixel B[n] to the difference of input
     pixels, A[n]-A[n-1]. A similarly crude way to fuzz is to set B[n]
     to the average of input pixels, 1/2*A[n]+1/2*A[n-1]. In each case
     the output is a weighted sum of input pixels, a "convolution". One
     important characteristic of such filters is that a sinusoid going
     in produces a sinusoid coming out, one of the same frequency. Thus
     the Fourier transform, which decomposes a signal into sinusoids of
     various frequencies, is the key to analysis of these filters. The
     simplest (and most efficient) way to handle the two dimensions of
     images is to operate on first the rows then the columns (or vice
     versa). Fourier transforms and many filters allow this separation.

     A filter is linear if it satisfies two simple relations between the
     input and output: scaling the input by a factor scales the output
     by the same factor, and the sum of two inputs gives the sum of the
     two outputs. A filter is shift invariant if shifting the input up,
     down, left, or right merely shifts the output the same way. When a
     filter is both linear and shift invariant, it can be implemented as
     a convolution, a weighted sum. If you find the output of the filter
     when the input is a single pixel with value one in a sea of zeros,
     you will know all the weights. This output is the impulse response
     of the filter. The Fourier transform of the impulse response gives
     the frequency response of the filter. The pattern of weights read
     off from the impulse response gives the filter kernel, which will
     usually be displayed (for image filters) as a 2D stencil array, and
     it is almost always symmetric around the center. For example, the
     following filter, approximating a Laplacian (and used for detecting
     edges), is centered on the negative value.
             1/6     4/6     1/6
             4/6   -20/6     4/6
             1/6     4/6     1/6
     The symmetry allows a streamlined implementation. Suppose the input
     image is in A, and the output is to go into B. Then compute
         B[i][j] = (A[i-1][j-1]+A[i-1][j+1]+A[i+1][j-1]+A[i+1][j+1]
                    +4.0*(A[i-1][j]+A[i][j-1]+A[i][j+1]+A[i+1][j])
                    -20.0*A[i][j])/6.0

     Ideal blurring is uniform in all directions, in other words it has
     circular symmetry. Gaussian blurs are popular, but the obvious code
     is slow for wide blurs. A cheap alternative is the following filter
     (written for rows, but then applied to the columns as well).
         B[i][j] = ((A[i][j]*2+A[i][j-1]+A[i][j+1])*4
                     +A[i][j-1]+A[i][j+1]-A[i][j-3]-A[i][j+3])/16
     For sharpening, subtract the results from the original image, which
     is equivalent to using the following.
         B[i][j] = ((A[i][j]*2-A[i][j-1]-A[i][j+1])*4
                     -A[i][j-1]-A[i][j+1]+A[i][j-3]+A[i][j+3])/16
     Credit for this filter goes to Ken Turkowski and Steve Gabriel.

     Reconstruction is impossible without some assumptions, and because
     of the importance of sinusoids in filtering it is traditional to
     assume the continuous image is made of sinusoids mixed together.
     That makes more sense for sounds, where signal processing began,
     than it does for images, especially computer images of character
     shapes, sharp surface features, and halftoned shading. As pointed
     out above, often image values cannot be negative, unlike sinusoids.
     Also, real world images contain noise. The best noise suppressors
     (and edge detectors) are, ironically, nonlinear filters.

     The simplest way to double the size of an image is to use each of
     the original pixels twice in its row and in its column. For much
     better results, try this instead. Put zeros between the original
     pixels, then use the blurring filter given a moment ago. But you
     might want to divide by 8 instead of 16 (since the zeros will dim
     the image otherwise). To instead shrink the image by half (in both
     vertical and horizontal), first apply the filter (dividing by 16),
     then throw away every other pixel. Notice that there are obvious
     optimizations involving arithmetic with powers of two, zeros which
     are in known locations, and pixels which will be discarded.

----------------------------------------------------------------------
Subject 3.06: How do I map a texture on to a shape?

    Paul S. Heckbert, "Survey of Texture Mapping", IEEE Computer
    Graphics and Applications V6, #11, Nov. 1986, pp 56-67 revised
    from Graphics Interface '86 version

    Eric A. Bier and Kenneth R. Sloan, Jr., "Two-Part Texture
    Mappings", IEEE Computer Graphics and Applications V6 #9, Sept.
    1986, pp 40-53 (projection parameterizations)

----------------------------------------------------------------------
Subject 3.07: How do I detect a 'corner' in a collection of points?

    [Currently empty entry.]

----------------------------------------------------------------------
Subject 3.08: Where do I get source to display (raster font format)?

    ftp://oak.oakland.edu/SimTel/msdos/graphics
    See also James Murray's graphics file formats FAQ:
        http://www.ora.com/centers/gff/gff-faq/index.htm

----------------------------------------------------------------------
Subject 3.09: What is morphing/how is it done?

    See [Anderson], Chapter 3, page 59 - 90.

----------------------------------------------------------------------
Subject 3.10: How do I quickly draw a filled triangle?

    The easiest way is to render each line separately into an edge
    buffer. This buffer is a structure which looks like this (in C):

        struct { int xmin, xmax; } edgebuffer[YDIM];

    There is one entry for each scan line on the screen, and each
    entry is to be interpreted as a horizontal line to be drawn from
    xmin to xmax.

    Since most people who ask this question are trying to write fast
    games on the PC, I'll tell you where to find code.  Look at:

        ftp::/ftp.uwp.edu/pub/msdos/demos/programming/source
        ftp::/ftp.luth.se/pub/msdos/demos (Sweden)
        ftp::/NCTUCCCA.edu.tw:/PC/uwp/demos
        http://www.wit.com:/mirrors/uwp/pub/msdos/demos
        ftp::/ftp.cdrom.com:/demos

----------------------------------------------------------------------
Subject 3.11: 3D Noise functions and turbulence in Solid texturing.

    See:
        ftp://gondwana.ecr.mu.oz.au/pub/siggraph92_C23.shar.gz
        ftp://ftp.cis.ohio-state.edu/pub/siggraph92/siggraph92_C23.shar

    In it there are implementations of Perlin's noise and turbulence
    functions, (By the man himself) as well as Lewis' sparse
    convolution noise function (by D. Peachey) There is also some of
    other stuff in there (Musgrave's Earth texture functions, and some
    stuff on animating gases by Ebert).

    SPD (Standard Procedural Databases) package:
        ftp://avalon.chinalake.navy.mil/utils/SPD/SPD33f4.tar.Z
        ftp://avalon.chinalake.navy.mil/utils/SPD/spd33f4.zip.
        Now moved to http://www.viewpoint.com/

    References:

    [Ebert]
    Noise, Hypertexture, Antialiasing and Gesture, (Ken Perlin) in
    Chapter 6, (p.193-), The disk accompanying the book is available
    from
        ftp://archive.cs.umbc.edu/pub/texture.

    For more info on this text/code see:
        http://www.cs.umbc.edu/~ebert/book/book.html

    For examples from a current course based on this book, see:
        http://www.seas.gwu.edu/graphics/ProcTexCourse/

    [Watt:Animation]
    Three-dimensional Nocie, Chapter 7.2.1
    Simulating turbulance, Chapter 7.2.2

----------------------------------------------------------------------
Subject 3.12: How do I generate realistic sythetic textures?

    For fractal mountains, trees and sea-shells:

      SPD (Standard Procedural Databases) package:
        ftp://avalon.chinalake.navy.mil/utils/SPD/SPD33f4.tar.Z
        ftp://avalon.chinalake.navy.mil/utils/SPD/spd33f4.zip.
        Now moved to http://www.viewpoint.com/

    Reaction-Diffusion Algorithms:
      For an illustartion of the parameter space of a reaction
      diffusion system, check out the Xmorphia page at
        http://www.ccsf.caltech.edu/ismap/image.html

    References:

    [Ebert]
    Entire book devoted to this subject, with RenderMan(TM) and C code.

    [Watt:Animation]
    Procedural texture mapping and modelling, Chapter 7

    "Generating Textures on Arbitrary Surfaces Using Reaction-Diffusion"
     Greg Turk,   Computer Graphics, Vol. 25, No. 4, pp. 289-298
     July 1991 (SIGGRAPH '91)
        http://www.cs.unc.edu:80/~turk/reaction_diffusion/reaction_diffusion.html

    A list of procedural texture synthesis related web pages
         http://www.threedgraphics.com/pixelloom/tex_synth.html

----------------------------------------------------------------------
Subject 3.13: How do I convert between color models (RGB, HLS, CMYK, CIE etc)?

    References:
    [Watt:3D] pp. 313-354
    [Foley]   pp. 563-603

----------------------------------------------------------------------
Subject 3.14: How is "GIF" pronounced?
 
   "GIF" is an acronymn for "Graphics Interchange Format."  Despite the
   hard "G" in "Graphics," GIF is pronounced "JIF."  Although we don't
   have a direct quote from the official CompuServe specification
   released June 1987, here is a quote from related CompuServe documentation,
   for CompuShow, a DOS-based image viewer used shortly thereafter: 
     "The GIF (Graphics Interchange Format), pronounced "JIF", was 
      designed by CompuServe ..."
   We also have a report that the principal author of the GIF spec,
   Bob Berry, pronounced it "JIF."
   Anyone with more definitive evidence should contact the FAQ maintainer.
 
----------------------------------------------------------------------
Section 4. Curve Computations
----------------------------------------------------------------------
Subject 4.01: How do I generate a Bezier curve that is parallel to another Bezier?

    You can't.  The only case where this is possible is when the
    Bezier can be represented by a straight line.  And then the
    parallel 'Bezier' can also be represented by a straight line.

    The situation is different for the broader class of rational
    Bezier curves.  For example, these can represent circular arcs,
    and a parallel offset is just a concentric circular arc, also
    representable as a rational Bezier.

----------------------------------------------------------------------
Subject 4.02: How do I split a Bezier at a specific value for t?

    A Bezier curve is a parametric polynomial function from the
    interval [0..1] to a space, usually 2-D or 3-D.  Common Bezier
    curves use cubic polynomials, so have the form

        f(t) = a3 t^3 + a2 t^2 + a1 t + a0,

    where the coefficients are points in 3-D. Blossoming converts this
    polynomial to a more helpful form.  Let s = 1-t, and think of
    tri-linear interpolation:

        F([s0,t0],[s1,t1],[s2,t2]) =
            s0(s1(s2 c30 + t2 c21) + t1(s2 c21 + t2 c12)) +
            t0(s1(s2 c21 + t2 c12) + t1(s2 c12 + t2 c03))
            =
            c30(s0 s1 s2) +
            c21(s0 s1 t2 + s0 t1 s2 + t0 s1 s2) +
            c12(s0 t1 t2 + t0 s1 t2 + t0 t1 s2) +
            c03(t0 t1 t2).

    The data points c30, c21, c12, and c03 have been used in such a
    way as to make the resulting function give the same value if any
    two arguments, say [s0,t0] and [s2,t2], are swapped. When [1-t,t]
    is used for all three arguments, the result is the cubic Bezier
    curve with those data points controlling it:

          f(t) = F([1-t,t],[1-t,t],[1-t,t])
               =  (1-t)^3 c30 +
                 3(1-t)^2 t c21 +
                 3(1-t) t^2 c12 +
                 t^3 c03.

    Notice that
           F([1,0],[1,0],[1,0]) = c30,
           F([1,0],[1,0],[0,1]) = c21,
           F([1,0],[0,1],[0,1]) = c12, _
           F([0,1],[0,1],[0,1]) = c03.

    In other words, cij is obtained by giving F argument t's i of
    which are 0 and j of which are 1. Since F is a linear polynomial
    in each argument, we can find f(t) using a series of simple steps.
    Begin with

        f000 = c30, f001 = c21, f011 = c12, f111 = c03.

    Then compute in succession:

        f00t = s f000 + t f001, f01t = s f001 + t f011,
        f11t = s f011 + t f111,
        f0tt = s f00t + t f01t, f1tt = s f01t + t f11t,
        fttt = s f0tt + t f1tt.

    This is the de Casteljau algorithm for computing f(t) = fttt.

    It also has split the curve for the intervals [0..t] and [t..1].
    The control points for the first interval are f000, f00t, f0tt,
    fttt, while those for the second interval are fttt, f1tt, f11t,
    f111.

    If you evaluate 3 F([1-t,t],[1-t,t],[-1,1]) you will get the
    derivate of f at t. Similarly, 2*3 F([1-t,t],[-1,1],[-1,1]) gives
    the second derivative of f at t, and finally using 1*2*3
    F([-1,1],[-1,1],[-1,1]) gives the third derivative.

    This procedure is easily generalized to different degrees,
    triangular patches, and B-spline curves.

----------------------------------------------------------------------
Subject 4.03: How do I find a t value at a specific point on a Bezier?

    In general, you'll need to find t closest to your search point.
    There are a number of ways you can do this. Look at [Gems I, 607],
    there's a chapter on finding the nearest point on the Bezier
    curve.  In my experience, digitizing the Bezier curve is an
    acceptable method. You can also try recursively subdividing the
    curve, see if you point is in the convex hull of the control
    points, and checking is the control points are close enough to a
    linear line segment and find the nearest point on the line
    segment, using linear interpolation and keeping track of the
    subdivision level, you'll be able to find t.

----------------------------------------------------------------------
Subject 4.04: How do I fit a Bezier curve to a circle?

    Interestingly enough, Bezier curves can approximate a circle but
    not perfectly fit a circle.

    Michael Goldapp, "Approximation of circular arcs by cubic
    polynomials" Computer Aided Geometric Design (#8 1991 pp.227-238)

    Tor Dokken and Morten Daehlen, "Good Approximations of circles by
    curvature-continuous Bezier curves" Computer Aided Geometric
    Design (#7 1990 pp. 33-41).

----------------------------------------------------------------------
Section 5. 3D computations
----------------------------------------------------------------------
Subject 5.01: How do I rotate a 3D point?

    Assuming you want to rotate vectors around the origin of your
    coordinate system. (If you want to rotate around some other point,
    subtract its coordinates from the point you are rotating, do the
    rotation, and then add back what you subtracted.) In 3-D, you need
    not only an angle, but also an axis. (In higher dimensions it gets
    much worse, very quickly.)  Actually, you need 3 independent
    numbers, and these come in a variety of flavors.  The flavor I
    recommend is unit quaternions: 4 numbers that square and add up to
    +1.  You can write these as [(x,y,z),w], with 4 real numbers, or
    [v,w], with v, a 3-D vector pointing along the axis. The concept
    of an axis is unique to 3-D. It is a line through the origin
    containing all the points which do not move during the rotation.
    So we know if we are turning forwards or back, we use a vector
    pointing out along the line. Suppose you want to use unit vector u
    as your axis, and rotate by 2t degrees.  (Yes, that's twice t.)
    Make a quaternion [u sin t, cos t]. You can use the quaternion --
    call it q -- directly on a vector v with quaternion
    multiplication, q v q^-1, or just convert the quaternion to a 3x3
    matrix M. If the components of q are {(x,y,z),w], then you want
    the matrix

        M = {{1-2(yy+zz),  2(xy-wz),  2(xz+wy)},
             {  2(xy+wz),1-2(xx+zz),  2(yz-wx)},
             {  2(xz-wy),  2(yz+wx),1-2(xx+yy)}}.

    Rotations, translations, and much more are explained in all basic
    computer graphics texts.  Quaternions are covered briefly in
    [Foley], and more extensively in several Graphics Gems, and the
    SIGGRAPH 85 proceedings.

    /* The following routine converts an angle and a unit axis vector
        * to a matrix, returning the corresponding unit quaternion at no
        * extra cost. It is written in such a way as to allow both fixed
        * point and floating point versions to be created by appropriate
        * definitions of FPOINT, ANGLE, VECTOR, QUAT, MATRIX, MUL, HALF,
        * TWICE, COS, SIN, ONE, and ZERO.
        * The following is an example of floating point definitions.
        #define FPOINT double
        #define ANGLE FPOINT
        #define VECTOR QUAT
        typedef struct {FPOINT x,y,z,w;} QUAT;
        enum Indices {X,Y,Z,W};
        typedef FPOINT MATRIX[4][4];
        #define MUL(a,b) ((a)*(b))
        #define HALF(a) ((a)*0.5)
        #define TWICE(a) ((a)*2.0)
        #define COS cos
        #define SIN sin
        #define ONE 1.0
        #define ZERO 0.0
    */
    QUAT MatrixFromAxisAngle(VECTOR axis, ANGLE theta, MATRIX m)
    {
        QUAT q;
        ANGLE halfTheta = HALF(theta);
        FPOINT cosHalfTheta = COS(halfTheta);
        FPOINT sinHalfTheta = SIN(halfTheta);
        FPOINT xs, ys, zs, wx, wy, wz, xx, xy, xz, yy, yz, zz;
        q.x = MUL(axis.x,sinHalfTheta);
        q.y = MUL(axis.y,sinHalfTheta);
        q.z = MUL(axis.z,sinHalfTheta);
        q.w = cosHalfTheta;
        xs = TWICE(q.x);  ys = TWICE(q.y);  zs = TWICE(q.z);
        wx = MUL(q.w,xs); wy = MUL(q.w,ys); wz = MUL(q.w,zs);
        xx = MUL(q.x,xs); xy = MUL(q.x,ys); xz = MUL(q.x,zs);
        yy = MUL(q.y,ys); yz = MUL(q.y,zs); zz = MUL(q.z,zs);
        m[X][X] = ONE - (yy + zz); m[X][Y] = xy - wz; m[X][Z] = xz + wy;
        m[Y][X] = xy + wz; m[Y][Y] = ONE - (xx + zz); m[Y][Z] = yz - wx;
        m[Z][X] = xz - wy; m[Z][Y] = yz + wx; m[Z][Z] = ONE - (xx + yy);
        /* Fill in remainder of 4x4 homogeneous transform matrix. */
        m[W][X] = m[W][Y] = m[W][Z] = m[X][W] = m[Y][W] = m[Z][W] = ZERO;
        m[W][W] = ONE;
        return (q);
    }
    /* The routine just given, MatrixFromAxisAngle, performs rotation about
        * an axis passing through the origin, so only a unit vector was needed
        * in addition to the angle. To rotate about an axis not containing the
        * origin, a point on the axis is also needed, as in the following. For
        * mathematical purity, the type POINT is used, but may be defined as:
        #define POINT VECTOR
    */
    QUAT MatrixFromAnyAxisAngle(POINT o, VECTOR axis, ANGLE theta, MATRIX m)
    {
        QUAT q;
        q = MatrixFromAxisAngle(axis,theta,m);
        m[X][W] = o.x-(MUL(m[X][X],o.x)+MUL(m[X][Y],o.y)+MUL(m[X][Z],o.z));
        m[Y][W] = o.y-(MUL(m[Y][X],o.x)+MUL(m[Y][Y],o.y)+MUL(m[Y][Z],o.z));
        m[Z][W] = o.x-(MUL(m[Z][X],o.x)+MUL(m[Z][Y],o.y)+MUL(m[Z][Z],o.z));
        return (q);
    }
    /* An axis can also be specified by a pair of points, with the direction
        * along the line assumed from the ordering of the points. Although both
        * the previous routines assumed the axis vector was unit length without
        * checking, this routine must cope with a more delicate possibility. If
        * the two points are identical, or even nearly so, the axis is unknown.
        * For now the auxiliary routine which makes a unit vector ignores that.
        * It needs definitions like the following for floating point.
        #define SQRT sqrt
        #define RECIPROCAL(a) (1.0/(a))
    */
    VECTOR Normalize(VECTOR v)
    {
        VECTOR u;
        FPOINT norm = MUL(v.x,v.x)+MUL(v.y,v.y)+MUL(v.z,v.z);
        /* Better to test for (near-)zero norm before taking reciprocal. */
        FPOINT scl = RECIPROCAL(SQRT(norm));
        u.x = MUL(v.x,scl); u.y = MUL(v.y,scl); u.z = MUL(v.z,scl);
        return (u);
    }
    QUAT MatrixFromPointsAngle(POINT o, POINT p, ANGLE theta, MATRIX m)
    {
        QUAT q;
        VECTOR diff, axis;
        diff.x = p.x-o.x; diff.y = p.y-o.y; diff.z = p.z-o.z;
        axis = Normalize(diff);
        return (MatrixFromAnyAxisAngle(o,axis,theta,m));
    }

----------------------------------------------------------------------
Subject 5.02: What is ARCBALL and where is the source?

    Arcball is a general purpose 3-D rotation controller described by
    Ken Shoemake in the Graphics Interface '92 Proceedings.  It
    features good behavior, easy implementation, cheap execution, and
    optional axis constraints. A Macintosh demo and electronic version
    of the original paper (Microsoft Word format) may be obtained from
    ftp::/ftp.cis.upenn.edu/pub/graphics.

    Complete source code appears in Graphics Gems IV pp. 175-192. A
    fairly complete sketch of the code appeared in the original
    article, in Graphics Interface 92 Proceedings, available from
    Morgan Kaufmann.

----------------------------------------------------------------------
Subject 5.03: How do I clip a polygon against a rectangle?

    This is the Sutherland-Hodgman algorithm, an old favorite that
    should be covered in any textbook. Any of the references listed in
    the FAQ should have it.  According to Vatti (q.v.)  "This
    [algorithm] produces degenerate edges in certain concave / self
    intersecting polygons that need to be removed as a special
    extension to the main algorithm" (though this is not a problem if
    all you are doing with the results is scan converting them.)

----------------------------------------------------------------------
Subject 5.04: How do I clip a polygon against another polygon?

    Klamer Schutte, [email-masked] has developed and implemented
    some code in C++ to perform clipping of two possibly concave 2-D
    polygons. A description can be found at:
    http://www.ph.tn.tudelft.nl:/People/klamer/clippoly_entry.html
    To compile the source code you will need a C++ compiler with templates,
    such as g++. The source code is available at:
    ftp://ftp.ph.tn.tudelft.nl/pub/klamer/clippoly.tar.gz
    See also http://members.xoom.com/msleonov/pbcomp.html, which extends
    the above to permit holes.

    Alan Murta released a polygon clipper library (in C) which uses a 
    modified version of the Vatti algorithm:
      http://www.cs.man.ac.uk/aig/staff/alan/software/index.html

    References:

    Weiler, K. "Polygon Comparison Using a Graph Representation", SIGGRAPH 80
    pg. 10-18

    Vatti, Bala R. "A Generic Solution to Polygon Clipping",
    Communications of the ACM, July 1992, Vol 35, No. 7, pg. 57-63

----------------------------------------------------------------------
Subject 5.05: How do I find the intersection of a line and a plane?

    If the plane is defined as:

        a*x + b*y + c*z + d = 0

    and the line is defined as:

        x = x1 + (x2 - x1)*t = x1 + i*t
        y = y1 + (y2 - y1)*t = y1 + j*t
        z = z1 + (z2 - z1)*t = z1 + k*t

    Then just substitute these into the plane equation. You end up
    with:

        t = - (a*x1 + b*y1 + c*z1 + d)/(a*i + b*j + c*k)

    When the denominator is zero, the line is contained in the plane 
    if the numerator is also zero (the point at t=0 satisfies the
    plane equation), otherwise the line is parallel to the plane.

----------------------------------------------------------------------
Subject 5.06: How do I determine the intersection between a ray and a triangle?

    First find the intersection between the ray and the plane in which
    the triangle is situated. Then see if the point of intersection is
    inside the triangle.
    Details may be found in [O'Rourke (C)] pp.226-238, whose code is at
       http://cs.smith.edu/~orourke/ .
    Efficient code complete with statistical tests is described in the Mo:ller-
    Trumbore paper in J. Graphics Tools (C code downloadable from there):
       http://www.acm.org/jgt/papers/MollerTrumbore97/
    See also the full paper:
       http://www.Graphics.Cornell.EDU/pubs/1997/MT97.html

----------------------------------------------------------------------
Subject 5.07: How do I determine the intersection between a ray and a sphere

    Given a ray defined as:

        x = x1 + (x2 - x1)*t = x1 + i*t
        y = y1 + (y2 - y1)*t = y1 + j*t
        z = z1 + (z2 - z1)*t = z1 + k*t

    and a sphere defined as:

        (x - l)**2 + (y - m)**2 + (z - n)**2 = r**2

    Substituting in gives the quadratic equation:

        a*t**2 + b*t + c = 0

    where:

        a = i**2 + j**2 + k**2
        b = 2*i*(x1 - l) + 2*j*(y1 - m) + 2*k*(z1 - n)
        c = (x1-l)**2 + (y1-m)**2 + (z1-n)**2 - r**2;

    If the discriminant of this equation is less than 0, the line does
    not intersect the sphere. If it is zero, the line is tangential to
    the sphere and if it is greater than zero it intersects at two
    points. Solving the equation and substituting the values of t into
    the ray equation will give you the points.

    Reference:

    [Glassner:RayTracing]

----------------------------------------------------------------------
Subject 5.08: How do I find the intersection of a ray and a Bezier surface?

    Joy I.K. and Bhetanabhotla M.N., "Ray tracing parametric surfaces
    utilizing numeric techniques and ray coherence", Computer
    Graphics, 16, 1986, 279-286

    Joy and Bhetanabhotla is only one approach of three major method
    classes: tessellation, direct computation, and a hybrid of the
    two.  Tessellation is relatively easy (you intersect the polygons
    making up the tessellation) and has no numerical problems, but can
    be inaccurate; direct is cheap on memory, but very expensive
    computationally, and can (and usually does) suffer from precision
    problems within the root solver; hybrids try to blend the two.

    Reference:

    [Glassner:RayTracing]

----------------------------------------------------------------------
|Subject 5.09: How do I ray trace caustics?

    See the work of Henrik Wann Jensen at 
       http://www.gk.dtu.dk/~hwj/papers/gi98.html
|      [Link stale -- can anyone update? -jor/1Dec99]

    @inproceedings{j-rcnls-96
    , author =      "Henrik Wann Jensen"
    , title =       "Rendering Caustics on Non-Lambertian Surfaces"
    , booktitle =   "Proc. Graphics Interface '96"
    , pages =       "116--121"
    , location =    "Toronto"
    , year =         1996
    }

    Some older references:

    An expensive answer:
    @InProceedings{mitchell-1992-illumination,
      author         = "Don P. Mitchell and Pat Hanrahan",
      title          = "Illumination From Curved Reflectors",
      year           = "1992",
      month          = "July",
      volume         = "26",
      booktitle      = "Computer Graphics (SIGGRAPH '92 Proceedings)",
      pages          = "283--291",
      keywords       = "caustics, interval arithmetic, ray tracing",
      editor         = "Edwin E. Catmull",
    }

    A cheat:
    @Article{inakage-1986-caustics,
      author         = "Masa Inakage",
      title          = "Caustics and Specular Reflection Models for
                        Spherical Objects and Lenses ",
      pages          = "379--383",
      journal        = "The Visual Computer",
      volume         = "2",
      number         = "6",
      year           = "1986",
      keywords       = "ray tracing effects",
    }

    Very specialized:
    @Article{yuan-1988-gemstone,
      author         = "Ying Yuan and Tosiyasu L. Kunii and Naota
                        Inamato and Lining Sun ",
      title          = "Gemstone Fire: Adaptive Dispersive Ray Tracing
                        of Polyhedrons",
      year           = "1988",
      month          = "November",
      journal        = "The Visual Computer",
      volume         = "4",
      number         = "5",
      pages          = "259--70",
      keywords       = "caustics",
    }

----------------------------------------------------------------------
Subject 5.10: What is the marching cubes algorithm?

    The marching cubes algorithm is used in volume rendering to
    construct an isosurface from a 3D field of values.

    The 2D analog would be to take an image, and for each pixel, set
    it to black if the value is below some threshold, and set it to
    white if it's above the threshold.  Then smooth the jagged black
    outlines by skinning them with lines.

    The marching cubes algorithm tests the corner of each cube (or
    voxel) in the scalar field as being either above or below a given
    threshold.  This yields a collection of boxes with classified
    corners.  Since there are eight corners with one of two states,
    there are 256 different possible combinations for each cube.
    Then, for each cube, you replace the cube with a surface that
    meets the classification of the cube.  For example, the following
    are some 2D examples showing the cubes and their associated
    surface.

        - ----- +       - ----- -       - ----- +       - ----- +
        |:::'   |       |:::::::|       |::::   |       |   ':::|
        |:'     |       |:::::::|       |::::   |       |.    ':|
        |       |       |       |       |::::   |       |::.    |
        + ----- +       + ----- +       - ----- +       + ----- -

    The result of the marching cubes algorithm is a smooth surface
    that approximates the isosurface that is constant along a given
    threshold. This is useful for displaying a volume of oil in a
    geological volume, for example.

    References:
    "Marching Cubes: A High Resolution 3D Surface  Construction Algorithm",
    William E. Lorensen and Harvey E. Cline,
    Computer Graphics (Proceedings of  SIGGRAPH '87), Vol. 21, No. 4, pp. 163-169.

    [Watt:Animation] pp. 302-305 and 313-321
    [Schroeder]
    James Sharman's description: http://www.exaflop.org/docs/marchcubes

----------------------------------------------------------------------
Subject 5.11: What is the status of the patent on the "marching cubes" algorithm?

    United States Patent Number: 4,710,876
    Date of Patent: Dec. 1, 1987
    Inventors: Harvey E. Cline, William E. Lorensen
    Assignee: General Electric Company
    Title: "System and Method for the Display of Surface Structures Contained
            Within the Interior Region of a Solid Body"
    Filed: Jun. 5, 1985
    http://www.patents.ibm.com/details?patent_number=4710876

    United States Patent Number: 4,885,688
    Date of Patent: Dec. 5, 1989
    Inventor: Carl R. Crawford
    Assignee: General Electric Company
    Title: "Minimization of Directed Points Generated in Three-Dimensional
            Dividing Cubes Method"
    Filed: Nov. 25, 1987
    http://www.patents.ibm.com/details?patent_number=4885688

----------------------------------------------------------------------
Subject 5.12: How do I do a hidden surface test (backface culling) with 3d points?

    Just define all points of all polygons in clockwise order.

            c = (x3*((z1*y2)-(y1*z2))+
                (y3*((x1*z2)-(z1*x2))+
                (z3*((y1*x2)-(x1*y2))+

    x1,y1,z1, x2,y2,z2, x3,y3,z3 = the first three points of the
    polygon.

    If c is positive the polygon is visible. If c is negative the
    polygon is invisible (or the other way).

----------------------------------------------------------------------
Subject 5.13: Where can I find algorithms for 3D collision detection?

    Check out "proxima", from Purdue, which is a C++ library for 3D
    collision detection for arbitrary polyhedra.  It's a nice system;
    the algorithms are sophisticated, but the code is of modest size.

        ftp://ftp.cs.purdue.edu/pub/pse/PROX/prox9.1.tar.Z

    For information about the I_COLLIDE 3D collision detection system
        http://www.cs.unc.edu/~geom/I_COLLIDE.html

    "Fast Collision Detection of Moving Convex Polyhedra", Rich Rabbitz,
    Graphics Gems IV, pages 83-109, includes source in C.

    SOLID: "a library for collision detection of three-dimensional
    objects undergoing rigid motion and deformation. SOLID is designed to 
    be used in interactive 3D graphics applications, and is especially 
    suited for collision detection of objects and worlds described in VRML.
    Written in standard C++, compiles under GNU g++ version 2.8.1 and 
    Visual C++ 5.0."  See:
        http://www.win.tue.nl/cs/tt/gino/solid/

----------------------------------------------------------------------
Subject 5.14: How do I perform basic viewing in 3d?

    We describe the shape and position of objects using numbers,
    usually (x,y,z) coordinates. For example, the corners of a cube
    might be {(0,0,0), (1,0,0), (1,1,0), (0,1,0), (0,0,1), (1,0,1),
    (1,1,1), (0,1,1)}. A deep understanding of what we are saying with
    these numbers requires mathematical study, but I will try to keep
    this simple. At the least, we must understand that we have
    designated some point in space as the origin--coordinates
    (0,0,0)--and marked off lines in 3 mutually perpendicular
    directions using equally spaced units to give us (x,y,z) values.
    It might be helpful to know if we are using 1 to mean 1 foot, 1
    meter, or 1 parsec; the numbers alone do not tell us.

    A picture on a screen is two steps removed from the 3D world it
    depicts. First, it is a 2D projection; and second, it is a finite
    resolution approximation to the infinitely precise projection. I
    will ignore the approximation (sampling) for now. To know what the
    projection looks like, we need to know where our viewpoint is, and
    where the plane of the projection is, both in the 3D world. Think
    of it as looking out a window into a scene. As artists discovered
    some 500 years ago, each point in the world appears to be at a
    point on the window. If you move your head or look out a different
    window, everything changes. When the mathematicians understood
    what the artists were doing, they invented perspective geometry.

    If your viewpoint is at the origin--(0,0,0)--and the window sits
    parallel to the x-y plane but at z=1, projection is no more than
    (x,y,z) in the world appears at (x/z,y/z,1) on the plane. Distant
    points will have large z values, causing them to shrink in the
    picture. That's perspective.

    The trick is to take an arbitrary viewpoint and plane, and
    transform the world so we have the simple viewing situation.
    There are two steps: move the viewpoint to the origin, then move
    the viewplane to the z=1 plane. If the viewpoint is at (vx,vy,vz),
    transform every point by the translation (x,y,z) -->
    (x-vx,y-vy,z-vz). This includes the viewpoint and the viewplane.
    Now we need to rotate so that the z axis points straight at the
    viewplane, then scale so it is 1 unit away.

    After all this, we may find ourselves looking out upside- down. It
    is traditional to specify some direction in the world or viewplane
    as "up", and rotate so the positive y axis points that way (as
    nearly as possible if it's a world vector). Finally, we have acted
    so far as if the window was the entire plane instead of a limited
    portal. A final shift and scale transforms coordinates in the
    plane to coordinates on the screen, so that a rectangular region
    of interest (our "window") in the plane fills a rectangular region
    of the screen (our "canvas" if you like).

    I have left out details of how you define and perform the rotation
    of the viewplane, but I'm sure someone else will be happy to
    supply those if there is demand. It requires knowing how to
    describe a plane, and how to rotate vectors to line up. Neither is
    difficult, but this is already using a lot of net space. One
    further practical difficulty is the need to clip away parts of the
    world behind us, so -(x,y,z) doesn't pop up at (x/z,y/z,1).
    (Notice the mathematics of projection alone would allow that!) But
    all the viewing transformations can be done using translation,
    rotation, scale, and a final perspective divide. If a 4x4
    homogeneous matrix is used, it can represent everything needed,
    which saves a lot of work.

----------------------------------------------------------------------
Subject 5.15: How do I optimize/simplify a 3D polygon mesh

    References:
    "Mesh Optimization" Hoppe, DeRose Duchamp, McDonald, Stuetzle,
     ACM COMPUTER GRAPHICS Proceedings, Annual Conference Series, 1993.

    "Re-Tiling Polygonal Surfaces",
     Greg Turk, ACM Computer Graphics, 26, 2, July 1992

    "Decimation of Triangle Meshes", Schroeder, Zarge, Lorensen,
    ACM Computer Graphics, 26, 2 July 1992

    "Simplification of Objects Rendered by Polygonal Approximations",
    Michael J. DeHaemer, Jr. and Michael J. Zyda, Computer & Graphics,
    Vol. 15, No. 2, 1991, Great Britain: Pergamon Press, pp. 175-184.

    "Topological Refinement Procedures for Triangular Finite Element
    Procedures", S. A. Cannan, S. N. Muthukrishnan and R. P. Phillips,
    Engineering With Computers, No. 12, p. 243-255, 1996.

    "Progressive Meshes", Hoppe, SIGGRAPH 96,
    http://research.microsoft.com/~hoppe/siggraph96pm/paper.htm
 
    [This list is in need of updating. -jor]

----------------------------------------------------------------------
Subject 5.16: How can I perform volume rendering?

    Two principal methods can be used:
    - Ray casting or front-to-back, where the volume is behind the
      projection plane. A ray is projected from each point in the projection
      plane through the volume. The ray accumulates the properties of each
      voxel it passes through.
    - Object order or back-to-front, where the projection plane is behind
      the volume. Each slice of the volume is projected on the projection
      plane, from the farest plane to the nearest plane.

    You can also use the marching-cubes algorithm, if the extraction of
    surfaces from the data set is easy to do (see Subject 5.10).

    Here is one algorithm to do front-to-back volume rendering:

    Set up a projection plane as a plane tangent to a sphere that encloses 
    the volume. From each pixel of the projection plane, cast a ray 
    through the volume by using a Bresenham 3D algorithm.
    The ray accumulates properties from each voxel intersected, stopping
    when the ray exits the volume. The pixel value on
    the projection plane determines the final color of the ray.

    For unshaded images (i.e., without gradient and light computations),
    if 	C  is the ray color            t  the ray transparency
        C' the new ray color           t' the new ray transparency
        Cv the voxel color             tv the voxel transparency
    then:
        C' = C + t*Cv       and        t' = t * tv
    with initial values: C = 0.0 and t = 1.0

    An alternate version: instead of C' = C + t*Cv , use :
        C' = C + t*Cv*(1-tv)^p     with p a float variable.
    Sometimes this gives the best results.
    When the ray has accumulated transparency, if it becomes negligible
    (e.g., t<0.1), the process can stop and proceed to the next ray.

    References:

    Bresenham 3D:
      - http://www.sct.gu.edu.au/~anthony/info/graphics/bresenham.procs
      - [Gems IV] p. 366
    Volume rendering:
      - [Watt:Animation] pp. 297-321
      - IEEE Computer Graphics and application
        Vol. 10, Nb. 2, March 1990 - pp. 24-53
      - "Volume Visualization"
        Arie Kaufman - Ed. IEEE Computer Society Press Tutorial
      - "Efficient Ray Tracing of Volume Data"
        Marc Levoy - ACM Transactions on Graphics, Vol. 9, Nb 3, July 1990

----------------------------------------------------------------------
Subject 5.17: Where can I get the spline description of the famous teapot etc.?

    See the Standard Procedural Databases software, whose official
    distribution site is 
        http://www.acm.org/tog/resources/SPD/
    This database contains much useful 3D code, including spline surface
    tessellation, for the teapot.

----------------------------------------------------------------------
Subject 5.18: How can the distance between two lines in space be computed?

    The following is robust C code from Seth Teller that computes the 
    "points of closest approach" on two 3D lines.  It also classifies 
    the lines as parallel, intersecting, or (the generic case) skew.
    What's listed below shows the main ideas; the full code is at
      http://graphics.lcs.mit.edu/~seth/geomlib/linelinecp.c

    // computes pB ON line B closest to line A
    // computes pA ON line A closest to line B
    // return: 0 if parallel; 1 if coincident; 2 if generic (i.e., skew)
    int
    line_line_closest_points3d (
        POINT *pA, POINT *pB,			// computed points
        const POINT *a, const VECTOR *adir,		// line A, point-normal form
        const POINT *b, const VECTOR *bdir )	// line B, point-normal form
    {
    static VECTOR Cdir, *cdir = &Cdir;
    static PLANE Ac, *ac = &Ac, Bc, *bc = &Bc;
    
        // connecting line is perpendicular to both
        vcross ( cdir, adir, bdir );
    
        // check for near-parallel lines
        if ( !vnorm( cdir ) )   {
            *pA = *a;	// all points are closest
            *pB = *b;
            return 0;	// degenerate: lines parallel
            }
    
        // form plane containing line A, parallel to cdir
        plane_from_two_vectors_and_point ( ac, cdir, adir, a );
    
        // form plane containing line B, parallel to cdir
        plane_from_two_vectors_and_point ( bc, cdir, bdir, b );
    
        // closest point on A is line A ^ bc
        intersect_line_plane ( pA, a, adir, bc );
    
        // closest point on B is line B ^ ac
        intersect_line_plane ( pB, b, bdir, ac );
    
        // distinguish intersecting, skew lines
        if ( edist( pA, pB ) < 1.0E-5F )
            return 1;     // coincident: lines intersect
        else
            return 2;     // distinct: lines skew
    }

    Also Dave Eberly has code for computing distance between various 
    geometric primitives, including MinLineLine(), at
      http://www.magic-software.com

----------------------------------------------------------------------
Subject 5.19: How can I compute the volume of a polyhedron?

    Assume that the surface is closed, every face is a triangle, and
    the vertices of each triangle are oriented ccw from the outside.
    Let Volume(t,p) be the signed volume of the tetrahedron formed
    by a point p and a triangle t.  This may be computed by a 4x4
    determinant, as in [O'Rourke (C), p.26].
            Choose an arbitrary point p (e.g., the origin), and compute
    the sum Volume(t_i,p) for every triangle t_i of the surface.  That
    is the volume of the object.  The justification for this claim
    is nontrivial, but is essentially the same as the justification for
    the computation of the area of a polygon (Subject 2.01).

    C Code available at http://cs.smith.edu/~orourke/ 
    and http://www.acm.org/jgt/papers/Mirtich96/ .

    For computing the volumes of n-d convex polytopes, 
    there is a C implementation by Bueeler and Enge of various
    algorithms available at

    http://www.Mathpool.Uni-Augsburg.DE/~enge/Volumen.html .

----------------------------------------------------------------------
Subject 5.20:  How can I decompose a polyhedron into convex pieces?
 
    Usually this problem is interpreted as seeking a collection
    of pairwise disjoint convex polyhedra whose set union is the
    original polyhedron P.  The following facts are known about
    this difficult problem:
 
       o  Not every polyhedron may be partitioned by diagonals into
          tetrahedra.  A counterexample is due to Scho:nhardt
          [O'Rourke (A), p.254].
 
       o  Determining whether a polyhedron may be so partitioned is
          NP-hard, a result due to Seidel & Ruppert [Disc. Comput. Geom. 
          7(3) 227-254 (1992).]
 
       o  Removing the restriction to diagonals, i.e., permitting
          so-called Steiner points, there are polyhedra of n vertices
          that require n^2 convex pieces in any decomposition.
          This was established by Chazelle [SIAM J. Comput. 
          13: 488-507 (1984)]. See also [O'Rourke (A), p.256]
 
       o  An algorithm of Palios & Chazelle guarantees at most
          O(n+r^2) pieces, where r is the number of reflex edges
          (i.e., grooves). [Disc. Comput. Geom. 5:505-526 (1990).]
 
       o  Barry Joe's geompack package, at 
             ftp://ftp.cs.ualberta.ca/pub/geompack,
          includes a 3D convex decomposition Fortran program. 
 
       o  There seems to be no other publicly available code.

----------------------------------------------------------------------
Subject 5.21: How can the circumsphere of a tetrahedron be computed?

    Let a, b, c, and d be the corners of the tetrahedron, with
    ax, ay, and az the components of a, and likewise for b, c, and d.  
    Let |a| denote the Euclidean norm of a, and let a x b denote the 
    cross product of a and b.  Then the center m and radius r of the
    circumsphere are given by
    
       |                                                                       |
       | |d-a|^2 [(b-a)x(c-a)] + |c-a|^2 [(d-a)x(b-a)] + |b-a|^2 [(c-a)x(d-a)] |
       |                                                                       |
    r= -------------------------------------------------------------------------
                                 | bx-ax  by-ay  bz-az |
                               2 | cx-ax  cy-ay  cz-az |
                                 | dx-ax  dy-ay  dz-az |
   
    and
   
           |d-a|^2 [(b-a)x(c-a)] + |c-a|^2 [(d-a)x(b-a)] + |b-a|^2 [(c-a)x(d-a)]
    m= a + ---------------------------------------------------------------------
                                   | bx-ax  by-ay  bz-az |
                                 2 | cx-ax  cy-ay  cz-az |
                                   | dx-ax  dy-ay  dz-az |
    
    Some notes on stability:
    
    - Note that the expression for r is purely a function of differences between
      coordinates.  The advantage is that the relative error incurred in the
      computation of r is also a function of the _differences_ between the
      vertices, and is not influenced by the _absolute_ coordinates of the
      vertices.  In most applications, vertices are usually nearer to each other
      than to the origin, so this property is advantageous.
    
      Similarly, the formula for m incurs roundoff error proportional to the
      differences between vertices, but not proportional to the absolute 
      coordinates of the vertices until the final addition.
    
    - These expressions are unstable in only one case:  if the denominator is
      close to zero.  This instability, which arises if the tetrahedron is 
      nearly degenerate, is unavoidable.  Depending on your application, you 
      may want to use exact arithmetic to compute the value of the determinant.
      See
        http://www.geom.umn.edu/software/cglist/alg.html
      or
        http://www.cs.cmu.edu/~quake/robust.html

----------------------------------------------------------------------
Subject 5.22: How do I determine if two triangles in 3D intersect?

    Let the two triangles be T1 and T2.  If T1 lies strictly to one side 
    of the plane containing T2, or T2 lies strictly to one side of the 
    plane containing T1, the triangles do not intersect.  Otherwise,
    compute the line of intersection L between the planes.  Let Ik
    be the interval (Tk inter L), k=1,2.  Either interval may be empty.
    T1 and T2 intersect iff I1 and I2 overlap.

    This method is decribed in Tomas Mo:ller, "A fast triangle-triangle
    intersection test," J. Graphics Tools 2(2) 25-30 1997.  C code at
    http://www.acm.org/jgt/papers/Moller97/

----------------------------------------------------------------------
Subject 5.23:  How can a 3D surface be reconstructed from a collection of points?

    This is a difficult problem.  There are two main variants: 
    (1) when the points are organized into parallel slices through 
    the object; (2) when the points are unorganized.  
    For (1), see this survey:
       D. Meyers, S. Skinner, K. Sloan. "Surfaces from Contours"
       ACM Trans. Graph. 11(3) Jul 1992, 228--258.
    Code is available at 
       http://www-sop.inria.fr/prisme/personnel/geiger/nuages.html
    For (2), see this paper:
       H. Hoppe, T. DeRose, T. Duchamp, J. McDonald, W. Stuetzle
       "Surface reconstruction from unorganized points"
       Proc. SIGGRAPH '92, 71--78.
    and P. Kumar's collection of links at
       http://members.tripod.com/~GeomWiz/www.sites.html

----------------------------------------------------------------------
Section 6. Geometric Structures and Mathematics
----------------------------------------------------------------------
Subject 6.01: Where can I get source for Voronoi/Delaunay triangulation?

    For 2-d Delaunay triangulation, try Shewchuk's triangle program.  It
    includes options for constrained triangulation and quality mesh
    generation.  It uses exact arithmetic.

    The Delaunay triangulation is equivalent to computing the convex hull
    of the points lifted to a paraboloid.  For n-d Delaunay triangulation
    try Clarkson's hull program (exact arithmetic) or Barber and Huhdanpaa's
    Qhull program (floating point arithmetic).  The hull program also
    computes Voronoi volumes and alpha shapes.  The Qhull program also
    computes 2-d Voronoi diagrams and n-d Voronoi vertices.  The output of
    both programs may be visualized with Geomview.

    There are many other codes for Delaunay triangulation and Voronoi
    diagrams.  See Amenta's list of computational geometry software.

    The Delaunay triangulation satisfies the following property: the
    circumcircle of each triangle is empty.  The Voronoi diagram is the
    closest-point map, i.e., each Voronoi cell identifies the points that
    are closest to an input site.  The Voronoi diagram is the dual of
    the Delaunay triangulation.  Both structures are defined for general
    dimension.  Delaunay triangulation is an important part of mesh
    generation.

    There is a FAQ of polyhedral computation explaining how to compute
    n-d Delaunay triangulation and n-d Voronoi diagram using a convex hull
    code, and how to use the linear programming technique to 
    determine the Voronoi cells adjacent to a given Voronoi cell
    efficiently for large scale or higher dimensional cases.

    Avis' lrs code uses the same file formats as cdd. It
    uses exact arithmetic and is useful
    for problems with very large output size, since it does not
    require storing the output.

    References:

    Amenta:   http://www.geom.umn.edu/software/cglist

    Avis:     ftp://mutt.cs.mcgill.ca/pub/C/lrs.html

    Barber &  http://www.geom.umn.edu/locate/qhull
    Huhdanpaa ftp://geom.umn.edu/pub/software/qhull.tar.Z

    Clarkson: http://cm.bell-labs.com/netlib/voronoi/hull.html
              ftp://cm.bell-labs.com/netlib/voronoi/hull.shar.Z

    Geomview: http://www.geom.umn.edu/locate/geomview
              ftp://geom.umn.edu/pub/software/geomview/

    Polyhedral Computation FAQ:
              http://www.ifor.math.ethz.ch/ifor/staff/fukuda/fukuda.html

    Shewchuk: http://www.cs.cmu.edu/~quake/triangle.html
              ftp://cm.bell-labs.com/netlib/voronoi/triangle.shar.Z

    [O' Rourke (C)] pp. 168-204

    [Preparata & Shamos] pp. 204-225

    Chew, L. P. 1987. "Constrained Delaunay Triangulations," Proc. Third
    Annual ACM Symposium on Computational Geometry.

    Chew, L. P. 1989. "Constrained Delaunay Triangulations," Algorithmica
    4:97-108. (UPDATED VERSION OF CHEW 1987.)

    Fang, T-P. and L. A. Piegl. 1994. "Algorithm for Constrained Delaunay
    Triangulation," The Visual Computer 10:255-265. (RECOMMENDED!)

    Frey, W. H. 1987. "Selective Refinement: A New Strategy for Automatic
    Node Placement in Graded Triangular Meshes," International Journal for
    Numerical Methods in Engineering 24:2183-2200.

    Guibas, L. and J. Stolfi. 1985. "Primitives for the Manipulation of
    General Subdivisions and the Computation of Voronoi Diagrams," ACM
    Transactions on Graphics 4(2):74-123.

    Karasick, M., D. Lieber, and L. R. Nackman. 1991. "Efficient Delaunay
    Triangulation Using Rational Arithmetic," ACM Transactions on Graphics
    10(1):71-91.

    Lischinski, D. 1994. "Incremental Delaunay Triangulation," Graphics
    Gems IV, P. S. Heckbert, Ed. Cambridge, MA: Academic Press Professional.
    (INCLUDES C++ SOURCE CODE -- THANK YOU, DANI!)

    [Okabe]

    Schuierer, S. 1989. "Delaunay Triangulation and the Radiosity
    Approach," Proc. Eurographics '89, W. Hansmann, F. R. A. Hopgood, and
    W. Strasser, Eds. Elsevier Science Publishers, 345-353.

    Subramanian, G., V. V. S. Raveendra, and M. G. Kamath. 1994. "Robust
    Boundary Triangulation and Delaunay Triangulation of Arbitrary
    Triangular Domains," International Journal for Numerical Methods in
    Engineering 37(10):1779-1789.

    Watson, D. F. and G. M. Philip. 1984. "Survey: Systematic
    Triangulations," Computer Vision, Graphics, and Image Processing
    26:217-223.

----------------------------------------------------------------------
Subject 6.02: Where do I get source for convex hull?

    For n-d convex hulls, try Clarkson's hull program (exact arithmetic)
    or Barber and Huhdanpaa's Qhull program (floating point arithmetic).
    Qhull handles numeric precision problems by merging facets. The output
    of both programs may be visualized with Geomview.

    In higher dimensions, the number of facets may be much smaller than
    the number of lower-dimensional features.  When this is the case,
    Fukuda's cdd program is much faster than Qhull or hull.

    There are many other codes for convex hulls.  See Amenta's list of
    computational geometry software.

    References:

    Amenta:   http://www.geom.umn.edu/software/cglist/

    Barber &  http://www.geom.umn.edu/locate/qhull
    Huhdanpaa ftp://geom.umn.edu/pub/software/qhull.tar.Z

    Clarkson: http://cm.bell-labs.com/netlib/voronoi/hull.html
              ftp://cm.bell-labs.com/netlib/voronoi/hull.shar.Z

    Geomview: http://www.geom.umn.edu/locate/geomview
              ftp://geom.umn.edu/pub/software/geomview/

    Fukuda:   http://www.ifor.math.ethz.ch/staff/fukuda/cdd_home/cdd.html
              ftp://ftp.ifor.math.ethz.ch/pub/fukuda/cdd/

    [O' Rourke (C)] pp. 70-167
    C code for Graham's algorithm on p.80-96.
    Three-dimensional convex hull discussed in Chapter 4 (p.113-67).
    C code for the incremental algorithm on p.130-60.

    [Preparata & Shamos] pp. 95-184

----------------------------------------------------------------------
Subject 6.03: Where do I get source for halfspace intersection?

    For n-d halfspace intersection, try Fukuda's cdd program or Barber
    and Huhdanpaa's Qhull program.  Both use floating point arithmetic.
    Fukuda includes code for exact arithmetic.  Qhull handles numeric
    precision problems by merging facets.

    Qhull computes halfspace intersection by computing a convex hull.
    The intersection of halfspaces about the origin is equivalent to the
    convex hull of the halfspace coefficients divided by their offsets.

    References:

    Barber &  http://www.geom.umn.edu/locate/qhull
    Huhdanpaa ftp://geom.umn.edu/pub/software/qhull.tar.Z

    Fukuda:   ftp://ifor13.ethz.ch/pub/fukuda/cdd/

    [Preparata & Shamos] pp. 315-320

----------------------------------------------------------------------
Subject 6.04: What are barycentric coordinates?
 
    	Let p1, p2, p3 be the three vertices (corners) of a closed 
    triangle T (in 2D or 3D or any dimension).  Let t1, t2, t3 be real 
    numbers that sum to 1, with each between 0 and 1:  t1 + t2 + t3 = 1,
    0 <= ti <= 1.  Then the point p = t1*p1 + t2*p2 + t3*p3 lies in
    the plane of T and is inside T.  The numbers (t1,t2,t3) are called the
    barycentric coordinates of p with respect to T. They uniquely identify
    p.  They can be viewed as masses placed at the vertices whose
    center of gravity is p.
    	For example, let p1=(0,0), p2=(1,0), p3=(3,2).  The
    barycentric coordinates (1/2,0,1/2) specify the point
    p = (0,0)/2 + 0*(1,0) + (3,2)/2 = (3/2,1), the midpoint of the p1-p3 
    edge.
    	If p is joined to the three vertices, T is partitioned
    into three triangles.  Call them T1, T2, T3, with Ti not incident
    to pi.  The areas of these triangles Ti are proportional to the
    barycentric coordinates ti of p.  
    
    Reference:
    [Coxeter, Intro. to Geometry, p.217].

----------------------------------------------------------------------
Subject 6.05: How can I generate a random point inside a triangle?

    	Use barycentric coordinates (see item 53) .  Let A, B, C be the
    three vertices of your triangle.  Any point P inside can be expressed
    uniquely as P = aA + bB + cC, where a+b+c=1 and a,b,c are each >= 0.
    Knowing a and b permits you to calculate c=1-a-b.  So if you can
    generate two random numbers a and b, each in [0,1], such that
    their sum <=1, you've got a random point in your triangle.
    	Generate random a and b independently and uniformly in [0,1] 
    (just divide the standard C rand() by its max value to get such a 
    random number.) If a+b>1, replace a by 1-a, b by 1-b.  Let c=1-a-b.  
    Then aA + bB + cC is uniformly distributed in triangle ABC: the reflection
    step a=1-a; b=1-b gives a point (a,b) uniformly distributed in the triangle
    (0,0)(1,0)(0,1), which is then mapped affinely to ABC.
    Now you have barycentric coordinates a,b,c.  Compute your point 
    P = aA + bB + cC.

    Reference: [Gems I], Turk, pp. 24-28.

----------------------------------------------------------------------
Subject 6.06: How do I evenly distribute N points on (tesselate) a sphere?
    
    "Evenly distributed" doesn't have a single definition.  There is a
    sense in which only the five Platonic solids achieve regular
    tesselations, as they are the only ones whose faces are regular
    and equal, with each vertex incident to the same number of faces.
    But generally "even distribution" focusses not so much on the
    induced tesselation, as it does on the distances and arrangement
    of the points/vertices.  For example, eight points can be placed
    on the sphere further away from one another than is achieved by
    the vertices of an inscribed cube: start with an inscribed cube,
    twist the top face 45 degrees about the north pole, and then
    move the top and bottom points along the surface towards the equator 
    a bit.

    The various definitions of "evenly distributed" lead into moderately 
    complex mathematics. This topic happens to be a FAQ on sci.math as well 
    as on comp.graphics.algorithms; a much more extensive and rigorous 
    discussion written by Dave Rusin can be found at:
    http://www.math.niu.edu/~rusin/known-math/95/sphere.faq

    A simple method of tesselating the sphere is repeated subdivision. An
    example starts with a unit octahedron. At each stage, every triangle in
    the tesselation is divided into 4 smaller triangles by creating 3 new
    vertices halfway along the edges. The new vertices are normalized,
    "pushing" them out to lie on the sphere. After N steps of subdivision,
    there will be 8 * 4^N triangles in the tesselation.

    A simple example of tesselation by subdivision is available at
       ftp://ftp.cs.unc.edu/pub/users/leech/sphere.c

    One frequently used definition of "evenness" is a distribution that 
    minimizes a 1/r potential energy function of all the points, so that in 
    a global sense points are as "far away" from each other as possible. 
    Starting from an arbitrary distribution, we can either use numerical 
    minimization algorithms or point repulsion, in which all the points 
    are considered to repel each other according to a 1/r^2 force law and 
    dynamics are simulated. The algorithm is run until some convergence 
    criterion is satisfied, and the resulting distribution is our approximation.
    
    Jon Leech developed code to do just this.  It is available at
    ftp://ftp.cs.unc.edu/pub/users/leech/points.tar.gz.
    See his README file for more information.  His distribution includes
    sample output files for various n < 300, which may be used off-the-shelf
    if that is all you need.

    Another method that is simpler than the above, but attains less
    uniformity, is based on spacing the points along a spiral that
    encircles the sphere.
    Code available from links at http://cs.smith.edu/~orourke/.

----------------------------------------------------------------------
Subject 6.07: What are coordinates for the vertices of an icosohedron?

    Data on various polyhedra is available at
    http://cm.bell-labs.com/netlib/polyhedra/index.html, or
    http://netlib.bell-labs.com/netlib/polyhedra/index.html, or
    http://www.netlib.org/polyhedra/index.html
    For the icosahedron, the twelve vertices are:
 
      (+- 1, 0, +-t), (0, +-t, +-1), and (+-t, +-1, 0)
 
    where t = (sqrt(5)-1)/2, the golden ratio.
    Reference: "Beyond the Third Dimension" by Banchoff, p.168.

----------------------------------------------------------------------
Subject 6.08: How do I generate random points on the surface of a sphere?

    There are several methods.  Perhaps the easiest to understand is
    the "rejection method":  generate random points in an origin-
    centered cube with opposite corners (r,r,r) and (-r,-r,-r).
    Reject any point p that falls outside of the sphere of radius r.
    Scale the vector to lie on the surface.  Because the cube to sphere
    volume ratio is pi/6, the average number of iterations before an
    acceptable vector is found is 6/pi = 1.90986.  This essentially
    doubles the effort, and makes this method slower than the "trig
    method."  A timing comparison conducted by Ken Sloan showed that
    the trig method runs in about 2/3's the time of the rejection method.
    He found that methods based on the use of normal distributions are
    twice as slow as the rejection method.

    The trig method.  This method works only in 3-space, but it is
    very fast.  It depends on the slightly counterintuitive fact (see
    proof below) that each of the three coordinates is uniformly
    distributed on [-1,1] (but the three are not independent,
    obviously).  Therefore, it suffices to choose one axis (Z, say)
    and generate a uniformly distributed value on that axis.  This
    constrains the chosen point to lie on a circle parallel to the
    X-Y plane, and the obvious trig method may be used to obtain the
    remaining coordinates.
    
    	(a) Choose z uniformly distributed in [-1,1].
    	(b) Choose t uniformly distributed on [0, 2*pi).
    	(c) Let r = sqrt(1-z^2).
    	(d) Let x = r * cos(t).
    	(e) Let y = r * sin(t).
    
    This method uses uniform deviates (faster to generate than normal
    deviates), and no set of coordinates is ever rejected.  
    
    Here is a proof of correctness for the fact that the z-coordinate is
    uniformly distributed.  The proof also works for the x- and y-
    coordinates, but note that this works only in 3-space.
    
    The area of a surface of revolution in 3-space is given by
    
    	A = 2 * pi * int_a^b f(x) * sqrt(1 + [f'(x}]^2) dx
    
    Consider a zone of a sphere of radius R.  Since we are integrating in
    the z direction, we have
    
    	f(z) = sqrt(R^2 - z^2)
    	f'(z) = -z / sqrt(R^2-z^2)
    
    	1 + [f'(z)]^2 = r^2 / (R^2-z^2)
    
    	A = 2 * pi * int_a^b sqrt(R^2-z^2) * R/sqrt(R^2-z^2) dz
    	  = 2 * pi * R int_a^b dz
    	  = 2 * pi * R * (b-a)
    	  = 2 * pi * R * h, 
    	  
    where h = b-a is the vertical height of the zone.  Notice how the integrand
    reduces to a constant.  The density is therefore uniform.

    Here is simple C code implementing the trig method:
    
    void SpherePoints(int n, double X[], double Y[], double Z[])
    {
      int i;
      double x, y, z, w, t;
    
      for( i=0; i< n; i++ ) {
        z = 2.0 * drand48() - 1.0;
        t = 2.0 * M_PI * drand48();
        w = sqrt( 1 - z*z );
        x = w * cos( t );
        y = w * sin( t );
        printf("i=%d:  x,y,z=\t%10.5lf\t%10.5lf\t%10.5lf\n", i, x,y,z);
        X[i] = x; Y[i] = y; Z[i] = z;
      }
    }
   
    A complete package is available at
    ftp://cs.smith.edu/pub/code/sphere.tar.gz (4K),
    reachable from http://cs.smith.edu/~orourke/ .

----------------------------------------------------------------------
Subject 6.09: What are Plucker coordinates?

    A common convention is to write umlauted u as "ue", so you'll also see
    "Pluecker". Lines in 3D can easily be given by listing the coordinates of
    two distinct points, for a total of six numbers. Or, they can be given as
    the coordinates of two distinct planes, eight numbers. What's wrong with
    these? Nothing; but we can do better. Pluecker coordinates are, in a sense,
    halfway between these extremes, and can trivially generate either. Neither
    extreme is as efficient as Pluecker coordinates for computations.

    When Pluecker coordinates generalize to Grassmann coordinates, as laid
    out beautifully in [Hodge], Chapter VII, the determinant definition
    is clearly the one to use. But 3D lines can use a simpler definition.
    Take two distinct points on a line, say

        P = (Px,Py,Pz)
        Q = (Qx,Qy,Qz)

    Think of these as vectors from the origin, if you like. The Pluecker
    coordinates for the line are essentially

        U = P - Q
        V = P x Q

    Except for a scale factor, which we ignore, U and V do not depend on the
    specific P and Q! Cross products are perpendicular to their factors, so we
    always have U.V = 0. In [Stolfi] lines have orientation, so are the same
    only if their Pluecker coordinates are related by a positive scale factor.

    As determinants of homogeneous coordinates, begin with the 4x2 matrix

        [ Px  Qx ] row x
        [ Py  Qy ] row y
        [ Pz  Qz ] row z
        [  1   1 ] row w

    Define Pluecker coordinate Gij as the determinant of rows i and j, in that
    order. Notice that Giw = Pi - Qi, which is Ui. Now let (i,j,k) be a cyclic
    permutation of (x,y,z), namely (x,y,z) or (y,z,x) or (z,x,y), and notice
    that Gij = Vk. Determinants are anti-symmetric in the rows, so Gij = -Gji.
    Thus all possible Pluecker line coordinates are either zero (if i=j) or
    components of U or V, perhaps with a sign change. Taking the w component
    of a vector as 0, the determinant form will operate just as well on a
    point P and vector U as on two points. We can also begin with a 2x4 matrix
    whose rows are the coefficients of homogeneous plane equations, E.P=0,
    from which come dual coordinates G'ij. Now if (h,i,j,k) is an even
    permutation of (x,y,z,w), then Ghi = G'jk. (Just swap U and V!)

    Got Pluecker, want points? No problem. At least one component of U is
    non-zero, say Ui, which is Giw. Create homogeneous points Pj = Gjw + Gij,
    and Qj = Gij. (Don't expect the P and Q that we started with, and don't
    expect w=1.) Want plane equations? Let (i,j,k,w) be an even permutation of
    (x,y,z,w), so G'jk = Giw. Then create Eh = G'hk, and Fh = G'jh.

    Example: Begin with P = (2,4,8) and Q = (2,3,5). Then U = (0,1,3) and
    V = (-4,6,-2). The direct determinant forms are Gxw=0, Gyw=1, Gzw=3,
    Gyz=-4, Gzx=6, Gxy=-2, and the dual forms are G'yz=0, G'zx=1, G'xy=3,
    G'xw=-4, G'yw=6, G'zw=-2. Take Uz = Gzw = G'xy = 3 as a suitable non-zero
    element. Then two planes meeting in the line are

        (G'xy  G'yy  G'zy  G'wy).P = 0
        (G'xx  G'xy  G'xz  G'xw).P = 0

    That is, a point P is on the line if it satisfies both these equations:

        3 Px + 0 Py + 0 Pz - 6 Pw = 0
        0 Px + 3 Py - 1 Pz - 4 Pw = 0

    We can also easily determine if two lines meet, or if not, how they pass.
    If U1 and V1 are the coordinates of line 1, U2 and V2, of line 2, we look
    at the sign of U1.V2 + V1.U2. If it's zero, they meet. The determinant form
    reveals even permutations of (x,y,z,w):
        G1xw G2yz + G1yw G2zx + G1zw G2xy + G1yz G2xw + G1zx p2yw + G1xy p2zw

    Two oriented lines L1 and L2 can interact in three different ways:
    L1 might intersect L2, L1 might go clockwise around L2, or L1 might go
    counterclockwise around L2. Here are some examples:

    	     | L2            | L2            | L2
    	L1   |          L1   |          L1   |
    	-----+----->    ----------->    -----|----->
    	     |               |               |
             V               V               V
    	 intersect    counterclockwise   clockwise
    	     | L2            | L2            | L2
    	L1   |          L1   |          L1   |
    	<----+-----     <----|------    <-----------
    	     |               |               |
             V               V               V

    The first and second rows are just different views of the same lines,
    once from the "front" and once from the "back." Here's what they might
    look like if you look straight down line L2 (shown here as a dot).

    	L1                               ---------->
    	-----o---->     L1   o           L1   o
    	                ---------->
    	 intersect    counterclockwise    clockwise

    The Pluecker coordinates of L1 and L2 give you a quick way to test
    which of the three it is.

    	cw:   U1.V2 + V1.U2 < 0
    	ccw:  U1.V2 + V1.U2 > 0
    	thru: U1.V2 + V1.U2 = 0

    So why is this useful? Suppose you want to test if a ray intersects
    a triangle in 3-space. One way to do this is to represent the ray and
    the edges of the triangle with Pluecker coordinates. The ray hits the
    triangle if and only if it hits one of the triangle's edges, or it's
    "clockwise" from all three edges, or it's "counterclockwise" from all
    three edges. For example...

          o  _
          | |\      		...in this picture, the ray
    	  |   \			is oriented counterclockwise
    	------ \ -->		from all three edges, so it
    	  |     \ 		must intersect the triangle.
    	  v      \
    	  o-----> o

    Using Pluecker coordinates, ray shooting tests like this take only
    a few lines of code.

    Grassmann coordinates allow similar methods to be used for points,
    lines, planes, and so on, and in a space of any dimension. Consult
    [Hodge] for a thorough discussion of the theory, [Stolfi] for
    a little theory with a concise implementation for low dimensions
    (see Subj. 0.04), and this article for different but equally lucid
    description:
      J. Erickson, Pluecker coordinates, Ray Tracing News 10(3) 1997,
      http://www.acm.org/tog/resources/RTNews/html/rtnv10n3.html .

----------------------------------------------------------------------
Section 7. Contributors
----------------------------------------------------------------------
Subject 7.01: How can you contribute to this FAQ?

    Send email to [email-masked] with your suggestions, possible
    topics, corrections, or pointers to information.  

----------------------------------------------------------------------
|Subject 7.02: Contributors.  Who made this all possible.

    Jens Alfke <[email-masked]>
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