Perceptual Error Optimization for Monte Carlo Rendering

Author:

Chizhov Vassillen1,Georgiev Iliyan2,Myszkowski Karol3ORCID,Singh Gurprit3ORCID

Affiliation:

1. MIA Group, Saarland University, Max-Planck-Institut für Informatik, Saarbrücken, Germany

2. Autodesk, London, United Kingdom

3. Max-Planck-Institut für Informatik, Saarbrücken, Germany

Abstract

Synthesizing realistic images involves computing high-dimensional light-transport integrals. In practice, these integrals are numerically estimated via Monte Carlo integration. The error of this estimation manifests itself as conspicuous aliasing or noise. To ameliorate such artifacts and improve image fidelity, we propose a perception-oriented framework to optimize the error of Monte Carlo rendering. We leverage models based on human perception from the halftoning literature. The result is an optimization problem whose solution distributes the error as visually pleasing blue noise in image space. To find solutions, we present a set of algorithms that provide varying trade-offs between quality and speed, showing substantial improvements over prior state of the art. We perform evaluations using quantitative and error metrics and provide extensive supplemental material to demonstrate the perceptual improvements achieved by our methods.

Funder

European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Graphics and Computer-Aided Design

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Blue noise for diffusion models;Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers '24;2024-07-13

2. FAST: Filter-Adapted Spatio-Temporal Sampling for Real-Time Rendering;Proceedings of the ACM on Computer Graphics and Interactive Techniques;2024-05-11

3. Perceptual error optimization for Monte Carlo animation rendering;SIGGRAPH Asia 2023 Conference Papers;2023-12-10

4. Scratch-based Reflection Art via Differentiable Rendering;ACM Transactions on Graphics;2023-07-26

5. Scalable Multi-Class Sampling via Filtered Sliced Optimal Transport;ACM Transactions on Graphics;2022-11-30

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