Scalable multi-class sampling via filtered sliced optimal transport
SIGGRAPH Asia 2022 (journal)
Abstract
We propose a multi-class point optimization formulation based on continuous Wasserstein barycenters. Our formulation is designed to handle hundreds to thousands of optimization objectives and comes with a practical optimization scheme. We demonstrate the effectiveness of our framework on various sampling applications like stippling, object placement, and Monte-Carlo integration. We a derive multi-class error bound for perceptual rendering error which can be minimized using our optimization.
Downloads and links
- paper (PDF, 36 MB)
- supplemental document (PDF, 32 MB)
- supplemental results – interactive image comparisons
- fast-forward video (MP4, 6.6 MB)
- summary video (MP4, 27 MB)
- talk video (MP4, 60 MB)
- code – reference implementation
- citation (BIB)
Media
Fast-forward video
Summary video
Talk video
BibTeX reference
@article{Salaun:2022:ScalableMultiClassSampling, author = {Corentin Sala\"un and Iliyan Georgiev and Hans-Peter Seidel and Gurprit Singh}, title = {Scalable multi-class sampling via filtered sliced optimal transport}, journal = {ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia)}, year = {2022}, volume = {41}, number = {6}, doi = {10.1145/3550454.3555484} }