Cache-friendly micro-jittered sampling
DUFAY, Arthur
Melting the frontiers between Light, Shape and Matter [MANAO]
Technicolor R & I [Cesson Sévigné]
Laboratoire Photonique, Numérique et Nanosciences [LP2N]
Melting the frontiers between Light, Shape and Matter [MANAO]
Technicolor R & I [Cesson Sévigné]
Laboratoire Photonique, Numérique et Nanosciences [LP2N]
PACANOWSKI, Romain
Laboratoire Photonique, Numérique et Nanosciences [LP2N]
Melting the frontiers between Light, Shape and Matter [MANAO]
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Laboratoire Photonique, Numérique et Nanosciences [LP2N]
Melting the frontiers between Light, Shape and Matter [MANAO]
DUFAY, Arthur
Melting the frontiers between Light, Shape and Matter [MANAO]
Technicolor R & I [Cesson Sévigné]
Laboratoire Photonique, Numérique et Nanosciences [LP2N]
Melting the frontiers between Light, Shape and Matter [MANAO]
Technicolor R & I [Cesson Sévigné]
Laboratoire Photonique, Numérique et Nanosciences [LP2N]
PACANOWSKI, Romain
Laboratoire Photonique, Numérique et Nanosciences [LP2N]
Melting the frontiers between Light, Shape and Matter [MANAO]
Laboratoire Photonique, Numérique et Nanosciences [LP2N]
Melting the frontiers between Light, Shape and Matter [MANAO]
GRANIER, Xavier
Laboratoire Photonique, Numérique et Nanosciences [LP2N]
Melting the frontiers between Light, Shape and Matter [MANAO]
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Laboratoire Photonique, Numérique et Nanosciences [LP2N]
Melting the frontiers between Light, Shape and Matter [MANAO]
Langue
en
Communication dans un congrès avec actes
Ce document a été publié dans
SIGGRAPH 2016, 2016-07-24, Anaheim.
Résumé en anglais
Monte-Carlo integration techniques for global illumination are popular on GPUs thanks to their massive parallel architecture, but efficient implementation remains challenging. The use of randomly de-correlated low-discrepancy ...Lire la suite >
Monte-Carlo integration techniques for global illumination are popular on GPUs thanks to their massive parallel architecture, but efficient implementation remains challenging. The use of randomly de-correlated low-discrepancy sequences in the path-tracing algorithm allows faster visual convergence. However, the parallel tracing of incoherent rays often results in poor memory cache utilization, reducing the ray bandwidth efficiency. Interleaved sampling [Keller et al. 2001] partially solves this problem, by using a small set of distributions split in coherent ray-tracing passes, but the solution is prone to structured noise. On the other hand, ray-reordering methods [Pharr et al. 1997] group stochastic rays into coherent ray packets but their implementation add an additional sorting cost on the GPU [Moon et al. 2010] [Garanzha and Loop 2010]. We introduce a micro-jittering technique for faster multi-dimensional Monte-Carlo integration in ray-based rendering engines. Our method, improves ray coherency between GPU threads using a slightly altered low-discrepancy sequence rather than using ray-reordering methods. Compatible with any low-discrepancy sequence and independent of the importance sampling strategy, our method achieves comparable visual quality with classic de-correlation methods, like Cranley-Patterson rotation [Kollig and Keller 2002], while reducing rendering times in all scenarios.< Réduire
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