Perceptual error optimization for Monte Carlo animation rendering

Misa Korac*1, Corentin Salaun*2, Iliyan Georgiev3, Pascal Grittmann1, Philipp Slusallek1, Karol Myszkowski2, Gurprit Singh2
(*These authors have contributed equally to this work)
1Saarland University and DFKI, Germany 2Max Planck Institute for Informatics, Germany 3Adobe, UK
SIGGRAPH Asia 2023
We propose an optimization framework to obtain perceptually pleasing error distribution in Monte Carlo animation rendering. The output of our algorithm is a sample set spanning multiple image pixels and frames. Here we show an image of a 30-frame sequence rendered with 1 sample/pixel per frame. We display a version of the animation filtered temporally using perceptually temporal kernels, to mimic its perception at one time instant. On the right we a show spatial (XY) crop and a spatio-temporal (XT) slice, along with the power spectra (DFT) of their corresponding error images. Our error distribution exhibits better blue-noise properties than that of previous work.


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.


paper (author version) / supplemental document (author version) / source code (coming soon)

HTML viewer / animated sequences / precomputed tiles


This project has received funding from the European Union’s Horizon 2020 research and innovation program under Marie Skłodowska Curie 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).

Copyright Disclaimer

The Author(s) / ACM. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record is available at

Imprint / Data Protection