Corentin Salaün

Corentin Salaün

PhD student at Max-Planck-Institut für Informatik

Perceptual error optimization for Monte Carlo animation rendering

Miša Korać, Corentin Salaün, Iliyan Georgiev, Pascal Grittmann, Philipp Slusallek, Gurprit Singh

ACM Siggraph asia 2023 (Conference track)
Published in SIGGRAPH Asia 2023 Conference Papers, 2023

Teaser

Abstract

Independently estimating pixel values in Monte Carlo rendering results in a perceptually sub-optimal white-noise distribution of error in image space. Recent works have shown that perceptual fidelity can be improved significantly by distributing pixel error as blue noise instead. Most such works have focused on static images, ignoring the temporal perceptual effects of animation display. We extend prior formulations to simultaneously consider the spatial and temporal domains, and perform an analysis to motivate a perceptually better spatio-temporal error distribution. We then propose a practical error optimization algorithm for spatio-temporal rendering and demonstrate its effectiveness in various configurations.

BibTeX reference

@inproceedings{Korac:2023:PerceptualErrorOptimizationAnimation,
    author = {Mi\v{s}a Kora\'{c} and Corentin Sala\"{u}n and Iliyan Georgiev and Pascal Grittmann and Philipp Slusallek and Karol Myszkowski and Gurprit Singh},
    title = {Perceptual error optimization for Monte Carlo animation rendering},
    booktitle = {ACM SIGGRAPH Asia 2023 Conference Proceedings},
    year = {2023},
    doi = {10.1145/3610548.3618146},
    isbn = {979-8-4007-0315-7/23/12}
}

Acknowledgements

This project has received funding from the European Union’s Horizon 2020 research and innovation program under Marie SkłodowskaCurie grant agreement no. 956585. We thank the anonymous reviewers for their feedback, and the authors of the following scenes: julioras3d (Chopper), NewSee2l035 (Modern Hall), Benedikt Bitterli (Utah Teapot), Wig42 (Living Room), and Jay Hardy (White Room).