Silence! Eliminate the Noise
Thursday, 9 August 9:00 AM - 10:30 AM | Los Angeles Convention Center, Room 408A
Session Chair: Pete Segal, Luxololgy, LLC
Silence! Eliminate the Noise
Importance Sampling for Hair Scattering
An importance sampling method for the bidirectional scattering distribution function (bsdf) of hair. The main contribution of this work is to provide a sampling algorithm for hair scattering that is effective (at reducing noise), robust, simple to implement, and efficient to evaluate.
Jiawei Ou
Dartmouth College/DreamWorks Animation
Feng Xie
DreamWorks Animation
Parashar Krishnamachari
DreamWorks Animation
Fabio Pellacini
Dartmouth College/Sapienza University of Rome
Adaptive Noise Reduction for Progressive Photon Mapping
An adaptive noise-reduction technique for progressive photon mapping that reduces noise in the rendered image but still converges to the correct result.
Zhe Fu
University of California, San Diego
Henrik Jensen
University of California, San Diego
Progressive Volume Photon Tracing
A photon-tracing-based progressive rendering method for participating and non-participating media without any photon-map storage requirement. The method offers better rendering speed and better control of memory use.
Charly Collin
University of Central Florida
Mickaël Ribardière
IRISA Rennes
Rémi Cozot
IRISA Rennes
Kadi Bouatouch
IRISA Rennes
Volume-Aware Extinction Mapping
Volume-aware extinction maps provide a unified framework for efficient computation of light scattering in arbitrary participating media.
Pascal Gautron
Technicolor Research & Innovation
Cyril Delalandre
Technicolor Research & Innovation
Jean-Eudes Marvie
Technicolor Research & Innovation
Pascal Lecocq
Technicolor Research & Innovation