Ensemble metropolis light transport

Bashford-Rogers, Thomas; Paulo Santos, Luis; Marnerides, Demetris; Debattista, Kurt

Abstract

This article proposes a Markov Chain Monte Carlo (MCMC) rendering algorithm based on a family of guided transition kernels. The kernels exploit properties of ensembles of light transport paths, which are distributed according to the lighting in the scene, and utilize this information to make informed decisions for guiding local path sampling. Critically, our approach does not require caching distributions in world space, saving time and memory, yet it is able to make guided sampling decisions based on whole paths. We show how this can be implemented efficiently by organizing the paths in each ensemble and designing transition kernels for MCMC rendering based on a carefully chosen subset of paths from the ensemble. This algorithm is easy to parallelize and leads to improvements in variance when rendering a variety of scenes.

• Journal Article Type: Article
• Acceptance Date: Jul 21, 2021
• Online Publication Date: Dec 20, 2021
• Publication Date: 2022-02
• Deposit Date: Sep 1, 2021
• Publicly Available Date: Jan 21, 2022
• Journal: ACM Transactions on Graphics
• Print ISSN: 0730-0301
• Electronic ISSN: 1557-7368
• Publisher: Association for Computing Machinery (ACM)
• Peer Reviewed: Peer Reviewed
• Volume: 41
• Issue: 1
• Article Number: 5
• DOI: https://doi.org/10.1145/3472294
• Public URL: https://uwe-repository.worktribe.com/output/7729047