# Neural Importance Sampling of Many Lights

arXiv: https://arxiv.org/abs/2505.11729 · HTML: https://mlsanity.com/paper/2505.11729/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 47 references that are in the map.
- First posted on arXiv 16 May 2025.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Neural Importance Sampling](https://mlsanity.com/paper/1808.03856/) (2018)
- [Neural Parametric Mixtures for Path Guiding](https://mlsanity.com/paper/2504.04315/) (2025)
- [Online Neural Path Guiding with Normalized Anisotropic Spherical Gaussians](https://mlsanity.com/paper/2303.08064/) (2023)
- [Online path sampling control with progressive spatio-temporal filtering](https://mlsanity.com/paper/2005.07547/) (2020)
- [Importance Sampling of Many Lights with Reinforcement Lightcuts Learning](https://mlsanity.com/paper/1911.10217/) (2019)

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

- [PureLight: Learning Complex Luminaires with Light Tracing](https://mlsanity.com/paper/2606.04319/) (2026)

## Abstract (arXiv)

We propose a neural approach for estimating spatially varying light selection distributions to improve importance sampling in Monte Carlo rendering, particularly for complex scenes with many light sources. Our method uses a neural network to predict the light selection distribution at each shading point based on local information, trained by minimizing the KL-divergence between the learned and target distributions in an online manner. To efficiently manage hundreds or thousands of lights, we integrate our neural approach with light hierarchy techniques, where the network predicts cluster-level distributions and existing methods sample lights within clusters. Additionally, we introduce a residual learning strategy that leverages initial distributions from existing techniques, accelerating convergence during training. Our method achieves superior performance across diverse and challenging scenes.