Gradient‐Domain ReSTIR Path Tracing

Abstract Gradient‐domain rendering accelerates realistic image synthesis by also estimating pixel color differences, which helps reconstruct high frequencies in the image domain. Converged images still require many samples per pixel even with denoising, and to this date, no real‐time gradient‐domain rendering methods have been proposed. We enable gradient‐domain methods in real‐time rendering by spatiotemporal sample reuse with a novel path space extension in gradient image rendering. We further explore this concept by implementing ReSTIR G‐PT, ReSTIR gradient‐domain path tracing, and find that relative sparsity of the gradient image allows highly selective spatial reuse and real‐time frame rates. Our method outperforms the baseline methods visually and statistically.

Authors

Institutions

Publication Details

Journal
Computer Graphics Forum
Published
2026-04-10
DOI
https://doi.org/10.1111/cgf.70328
Primary Topic
Computer Graphics and Visualization Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Gradient‐Domain ReSTIR Path Tracing

Chris Wyman, Daqi Lin, Shuang Zhao, Markus Kettunen et al.
Computer Graphics Forum
Computer Graphics and Visualization Techniques
article

Gradient‐Domain ReSTIR Path Tracing

Chris Wyman, Daqi Lin, Shuang Zhao, Markus Kettunen, Lifan Wu, Yu‐Chen Wang
article en

Abstract

Abstract Gradient‐domain rendering accelerates realistic image synthesis by also estimating pixel color differences, which helps reconstruct high frequencies in the image domain. Converged images still require many samples per pixel even with denoising, and to this date, no real‐time gradient‐domain rendering methods have been proposed. We enable gradient‐domain methods in real‐time rendering by spatiotemporal sample reuse with a novel path space extension in gradient image rendering. We further explore this concept by implementing ReSTIR G‐PT, ReSTIR gradient‐domain path tracing, and find that relative sparsity of the gradient image allows highly selective spatial reuse and real‐time frame rates. Our method outperforms the baseline methods visually and statistically.

Computer Graphics Forum
University of Illinois Urbana-Champaign (US), University of California, Irvine (US)
Openalex Percentile: Top 17%
Computer Graphics and Visualization Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Gradient‐Domain ReSTIR Path Tracing — Chris Wyman, Daqi Lin, et al. · Computer Graphics Forum (2026) | TGRS Research Map | TGRS