Correlation‐Aware Multiple Importance Sampling for Bidirectional Rendering Algorithms

Grittmann, Pascal; Georgiev, Iliyan; Slusallek, Philipp

doi:10.1111/cgf.142628

Cited by 3 publications

(1 citation statement)

References 27 publications

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“…This produces a non-convex objective that is much harder to optimize. Further, RRS in a bidirectional context is problematic for MIS weighting, as the classic balance heuristic ignores the covariance introduced by splitting [Grittmann et al 2021;Popov et al 2015].…”

Section: Limitations and Future Workmentioning

confidence: 99%

Ears

et al. 2022

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Russian roulette and splitting are widely used techniques to increase the efficiency of Monte Carlo estimators. But, despite their popularity, there is little work on how to best apply them. Most existing approaches rely on simple heuristics based on, e.g., surface albedo and roughness. Their efficiency often hinges on user-controlled parameters. We instead iteratively learn optimal Russian roulette and splitting factors during rendering, using a simple and lightweight data structure. Given perfect estimates of variance and cost, our fixed-point iteration provably converges to the optimal Russian roulette and splitting factors that maximize the rendering efficiency. In our application to unidirectional path tracing, we achieve consistent and significant speed-ups over the state of the art.

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Section: Limitations and Future Workmentioning

confidence: 99%

Ears

et al. 2022

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Hypothesis Testing for Progressive Kernel Estimation and VCM Framework

Lin

Jia

et al. 2024

IEEE Trans. Visual. Comput. Graphics

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SPCBPT

Wang

2022

ACM Trans. Graph.

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Bidirectional path tracing (BDPT) can be accelerated by selecting appropriate light sub-paths for connection. However, existing algorithms need to perform frequent distribution reconstruction and have expensive overhead. We present a novel approach, SPCBPT, for probabilistic connections that constructs the light selection distribution in sub-path space. Our approach bins the sub-paths into multiple subspaces and keeps the sub-paths in the same subspace of low discrepancy, wherein the light sub-paths can be selected by a subspace-based two-stage sampling method, i.e., first sampling the light subspace and then resampling the light sub-paths within this subspace. The subspace-based distribution is free of reconstruction and provides efficient light selection at a very low cost. We also propose a method that considers the Multiple Importance Sampling (MIS) term in the light selection and thus obtain an MIS-aware distribution that can minimize the upper bound of variance of the combined estimator. Prior methods typically omit this MIS weights term. We evaluate our algorithm using various benchmarks, and the results show that our approach has superior performance and can significantly reduce the noise compared with the state-of-the-art method.

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Correlation‐Aware Multiple Importance Sampling for Bidirectional Rendering Algorithms

Cited by 3 publications

References 27 publications

Ears

Ears

Hypothesis Testing for Progressive Kernel Estimation and VCM Framework

SPCBPT

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