[Supplemental Document]
[Source Code]
[Interative Comparisons]
["Two Minute Papers" (YouTube)]
Abstract
Volume rendering under complex, dynamic lighting is challenging, especially if targeting real-time. To address this challenge, we extend a recent direct illumination sampling technique, spatiotemporal reservoir resampling, to multi-dimensional path space for volumetric media.
By fully evaluating just a single path sample per pixel, our volumetric path tracer shows unprecedented convergence. To achieve this, we properly estimate the chosen sample’s probability via approximate perfect importance sampling with spatiotemporal resampling. A key observation is recognizing that applying cheaper, biased techniques to approximate scattering along candidate paths (during resampling) does not add bias when shading. This allows us to combine transmittance evaluation techniques: cheap approximations where evaluations must occur many times for reuse, and unbiased methods for final, per-pixel evaluation.
With this reformulation, we achieve low-noise, interactive volumetric path tracing with arbitrary dynamic lighting, including volumetric emission, and maintain interactive performance even on high-resolution volumes. When paired with denoising, our low-noise sampling helps preserve smaller-scale volumetric details.
A volumetric bunny illuminated by a complex environment map and emissive logos. We compare our new volumetric ReSTIR with offline references and an equal-time baseline (combining decomposition tracking [Kutz et al. 2017] and residual ratio tracking [Novák et al. 2014]). We show our work with (left) single scattering in 55 ms and (right) three-bounce multiple scattering in 142 ms.
Video
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BibTeX
@article{Lin2021,
author = {Daqi Lin and Chris Wyman and Cem Yuksel},
title = {Fast Volume Rendering with Spatiotemporal Reservoir Resampling},
journal = {ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia 2021)},
year = {2021},
month = {dec},
volume = {40},
number = {6},
pages = {279:1--279:18},
articleno = {279},
numpages = {18},
url = {http://doi.acm.org/10.1145/3478513.3480499},
doi = {10.1145/3478513.3480499},
issn = {0730-0301},
publisher = {ACM Press},
address = {New York, NY, USA},
}