# Scalable multi-class sampling via filtered sliced optimal transport

arXiv: https://arxiv.org/abs/2211.04314 · HTML: https://mlsanity.com/paper/2211.04314/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Nov 2022.

## Built on by (most cited first, 3 of 3)

- [Blue noise for diffusion models](https://mlsanity.com/paper/2402.04930/) (2024)
- [2D Neural Fields with Learned Discontinuities](https://mlsanity.com/paper/2408.00771/) (2024)
- [Example-Based Sampling with Diffusion Models](https://mlsanity.com/paper/2302.05116/) (2023)

## Abstract (arXiv)

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. We provide source code at https://github.com/iribis/filtered-sliced-optimal-transport.