ReSTIR PG: Path Guiding with Spatiotemporally Resampled Paths
Zeng, Zheng Kettunen, Markus Wyman, Chris Wu, Lifan Ramamoorthi, Ravi Yan, Lingqi Lin, Daqi
Zeng, Zheng
Kettunen, Markus
Wyman, Chris
Wu, Lifan
Ramamoorthi, Ravi
Yan, Lingqi
Lin, Daqi
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Department
Computer Science
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Type
Conference proceeding
Date
2025
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Language
English
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Abstract
We present ReSTIR Path Guiding (ReSTIR-PG), a real-time method that extracts guiding distributions from resampled paths produced by ReSTIR and uses them to generate improved initial candidates for the next frame. While ReSTIR significantly reduces variance through spatiotemporal resampling, its effectiveness is ultimately limited by the quality of the initial candidates, which are often poorly distributed and introduce correlation artifacts. Our key observation is that ReSTIR’s accepted paths already approximate the target path contribution density, and that their bounce directions follow the ideal distribution for local path guiding – the product of incident radiance and the cosine-weighted BSDF. We exploit this structure to fit lightweight guiding distributions using each frame’s resampled paths by density estimation. Compared to conventional guiding based on raw path-traced samples, ReSTIR-PG closes the loop between guiding and resampling. Our method achieves lower variance, faster response time to scene change, reduced correlation artifacts, all while preserving real-time performance.
Citation
Z. Zeng et al., “ReSTIR PG: Path Guiding with Spatiotemporally Resampled Paths,” Proceedings of the SIGGRAPH Asia 2025 Conference Papers, pp. 1–11, Dec. 2025, doi: 10.1145/3757377.3763813
Source
Proceedings of the SIGGRAPH Asia 2025 Conference Papers
Conference
SIGGRAPH Asia 2025 Conference
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Source
SIGGRAPH Asia 2025 Conference
Publisher
Association for Computing Machinery