Work

Conditional Resampled Importance Sampling and ReSTIR

Year: 2023

Type: conference-paper

Abstract: Recent work on generalized resampled importance sampling (GRIS) enables importance-sampled Monte Carlo integration with random variable weights replacing the usual division by probability density. This enables very flexible spatiotemporal sample reuse, even if neighboring samples (e.g., light paths) have intractable probability densities. Unlike typical Monte Carlo integration, which samples according to some PDF, GRIS instead resamples existing samples. But resampling with GRIS assumes samples have tractable marginal contribution weights, which is problematic if reusing, for example, light subpaths from unidirectionally-sampled paths. Reusing such subpaths requires conditioning by (non-reused) segments of the path prefixes.

Source: SIGGRAPH Asia Conference Papers

DOI: 10.1145/3610548.3618245

Authors: Markus Kettunen, Daqi Lin, Ravi Ramamoorthi, Thomas Bashford-Rogers, Chris Wyman

Institutions: Nvidia (United States), University of California San Diego, University of Warwick

Language: English

Topic: Computer Graphics and Visualization Techniques

Subfield: Computer Graphics and Computer-Aided Design

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W4389539484

FWCI: 0.834

Cites: 27

Cited by: 12

Related to: 10