Eurographics Symposium on Rendering 2020
(Left) Comparison of Visibility Light Cache (VLC) against Light Hierarchy in Q2RTX. Through implicit handling of
the visibility term by explicitly tracking and sampling visible lights, we gain significant variance reduction.
(Right) Optimal sampling density for radiance field of a given surface point and approximation by Compressed
Directional Quadtree (CDQ) in 16 bytes. It is used for guiding rays towards high-energy directions.
Abstract
Good importance sampling is crucial for real-time path tracing where only low sample budgets are possible. We
present two efficient sampling techniques tailored for massively-parallel GPU path tracing which improve next
event estimation (NEE) for rendering with many light sources and sampling of indirect illumination. As sampling
density need to vary spatially, we use an octree structure in world space and introduce algorithms to
continuously adapt the partitioning and distribution of the sampling bugdet. Both sampling techniques exploit
temporal coherence by reusing samples from the previous frame: For NEE we collect unoccluded samples on light
sources and show how to deduplicate, but also diffuse this information to efficiently sample light sources in
the subsequent frame. For sampling indirect illumination, we present a compressed directional quadtree structure
which is iteratively adapted towards high-energy directions using samples from the previous frame. The updates
and rebuilding of all data structures takes about 1 ms in our test scenes, and adds about 6 ms at 1080p to the
path tracing time compared to using state-of-the-art light hierarchies and BRDF sampling. We show that this
additional effort reduces noise in terms of mean squared error by at least one order of magnitude in many
situations.
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Bibtex
@inproceedings {2020_tsr_nee_pg_rtpt,
booktitle = {Eurographics Symposium on Rendering - DL-only Track},
editor = {Dachsbacher, Carsten and Pharr, Matt},
title = {{Temporal Sample Reuse for Next Event Estimation and
Path Guiding for Real-Time Path Tracing}},
author = {Dittebrandt, Addis and
Hanika, Johannes and
Dachsbacher, Carsten},
year = {2020},
publisher = {The Eurographics Association},
DOI = {10.2312/sr.20201135}
}
