BIB-VERSION:: CS-TR-v2.0
ID:: ncstrl.dartmouthcs//TR2007-600
ENTRY:: July 18, 2007
ORGANIZATION:: Dartmouth College, Computer Science
TITLE:: Light-Based Sample Reduction Methods for Interactive Relighting of Scenes with Minute Geometric Scale
TYPE:: Technical Report (paper)
REVISION:: 1
AUTHOR:: Kerr, William B.
AUTHOR:: Pellacini, Fabio
DATE:: July 2007
RETRIEVAL:: For a paper copy, email <reports@cs.dartmouth.edu>
RETRIEVAL:: For a paper copy, write to
       Technical Report Librarian
       Department of Computer Science
       Dartmouth College
       6211 Sudikoff Laboratory
       Hanover, NH 03755-3510
       USA
RETRIEVAL:: PDF at http://www.cs.dartmouth.edu/reports/TR2007-600.pdf
ABSTRACT:: Rendering production-quality cinematic scenes requires high computational and temporal costs.  From an artist's perspective, one must wait for several hours for feedback on even minute changes of light positions and parameters.  Previous work approximates scenes so that adjustments on lights may be carried out with interactive feedback, so long as geometry and materials remain constant.  We build on these methods by proposing means by which objects with high geometric complexity at the subpixel level, such as hair and foliage, can be approximated for real-time cinematic relighting.  Our methods make no assumptions about the geometry or shaders in a scene, and as such are fully generalized.  We show that clustering techniques can greatly reduce multisampling, while still maintaining image fidelity at an error significantly lower than sparsely sampling without clustering, provided that no shadows are computed.  Scenes that produce noise-like shadow patterns when sparse shadow samples are taken suffer from additional error introduced by those shadows.  We present a viable solution to scalable scene approximation for lower sampling reolutions, provided a robust solution to shadow approximation for sub-pixel geomery can be provided in the future.
END:: ncstrl.dartmouthcs//TR2007-600