# Importance Sampling of Many Lights with Reinforcement Lightcuts Learning

arXiv: https://arxiv.org/abs/1911.10217 · HTML: https://mlsanity.com/paper/1911.10217/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Nov 2019.

## Built on by (most cited first, 1 of 1)

- [Neural Importance Sampling of Many Lights](https://mlsanity.com/paper/2505.11729/) (2025)

## Abstract (arXiv)

In this manuscript, we introduce a novel technique for sampling and integrating direct illumination in the presence of many lights. Unlike previous work, the presented technique importance samples the product distribution of radiance and visibility while using bounded memory footprint and very low sampling overhead. This is achieved by learning a compact approximation of the target distributions over both space and time, allowing to reuse and adapt the learnt distributions both spatially, within a frame, and temporally, across multiple frames. Finally, the technique is amenable to massive parallelization on GPUs and suitable for both offline and real-time rendering.