Perceptual error optimization for Monte Carlo rendering
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 tradeoffs 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.
Downloads and links
- paper (PDF, 44 MB)
- supplemental document (PDF, 11 MB)
- paper (ACM version) – on ACM Digital Library (open access)
- supplemental results – interactive image comparisons
- supplemental video (MP4, 218 MB)
- fast-forward video (MP4, 4.5 MB)
- talk video (MP4, 34 MB)
- talk video subtitles (SRT)
- Arxiv preprint
- citation (BIB)
Media
Supplemental video
Fast-forward video
Talk video
BibTeX reference
@article{Chizhov:2022:PerceptualErrorOptimization, author = {Vassillen Chizhov and Iliyan Georgiev and Karol Myszkowski and Gurprit Singh}, title = {Perceptual error optimization for Monte Carlo rendering}, journal = {ACM Trans. Graph.}, year = {2022}, volume = {41}, number = {3}, doi = {10.1145/3504002} }