Rao-Blackwellized Markov chain Monte Carlo Light Transport

2026-05-09

AI Summary

This work addresses the limited variance reduction achieved by traditional Rao-Blackwellization techniques—such as waste-recycling—in light transport simulation, which often results in high rendering noise and slow convergence. The authors propose a novel, efficient Rao-Blackwellization strategy tailored for general Metropolis-Hastings and Jump Restore light transport algorithms. By carefully leveraging auxiliary information while maintaining computational overhead within practical bounds, the method substantially reduces estimator variance. Experimental evaluations demonstrate that, under equal-time or equal-sample conditions, the proposed approach consistently outperforms existing methods across a range of complex light transport scenarios, yielding visibly lower noise levels and accelerated convergence.

Abstract

In light transport simulation, Markov chain Monte Carlo methods are particularly effective at exploring regions with complex lighting characteristics. However, estimator variance is a central concern across Monte Carlo methods in general. In light transport, high variance directly manifests as increased noise or, equivalently, longer rendering times at fixed image quality. Variance reduction techniques based on Rao-Blackwellization have proven particularly effective. In practice, however, the RB approach traditionally used in light transport, waste-recycling, can yield little to no measurable variance reduction, a fact we empirically confirm in this work. Motivated by this lack of effective variance reduction, we introduce a novel RB technique for the general-purpose Metropolis-Hastings algorithm that is computationally efficient and achieves substantial variance reduction. We show that this method consistently outperforms waste-recycling in terms of both variance reduction and convergence speed. Building on this result, we adapt the proposed RB approach to the recently introduced general-purpose Jump Restore algorithm, where it similarly achieves substantial variance reduction and accelerated convergence. Through extensive experiments in light transport simulation, we demonstrate that our \gls{rb} technique significantly outperforms the traditional approaches for both MH-based light transport algorithms and Jump Restore Light Transport, under both equal-time and equal-sample-count comparisons.

Problem

• light transport
• variance reduction
• Markov chain Monte Carlo
• Rao-Blackwellization
• rendering noise

Innovation

• Rao-Blackwellization
• variance reduction
• Metropolis-Hastings
• light transport simulation
• Jump Restore