# Neural Parametric Mixtures for Path Guiding

arXiv: https://arxiv.org/abs/2504.04315 · HTML: https://mlsanity.com/paper/2504.04315/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2025.

## Built on by (most cited first, 7 of 7)

- [Vertex Features for Neural Global Illumination](https://mlsanity.com/paper/2508.07852/) (2025)
- [Neural Importance Sampling of Many Lights](https://mlsanity.com/paper/2505.11729/) (2025)
- [Neural Path Guiding with Distribution Factorization](https://mlsanity.com/paper/2506.00839/) (2025)
- [Neural Cone Radiosity for Interactive Global Illumination with Glossy Materials](https://mlsanity.com/paper/2509.07522/) (2025)
- [PureLight: Learning Complex Luminaires with Light Tracing](https://mlsanity.com/paper/2606.04319/) (2026)
- [OctaOctree Neural Radiosity for Real-time Glossy Material Rendering](https://mlsanity.com/paper/2606.08469/) (2026)
- [RenderFormer++: Scalable and Physics-Informed Feed-Forward Neural Rendering](https://mlsanity.com/paper/2606.30380/) (2026)

## Abstract (arXiv)

Previous path guiding techniques typically rely on spatial subdivision structures to approximate directional target distributions, which may cause failure to capture spatio-directional correlations and introduce parallax issue. In this paper, we present Neural Parametric Mixtures (NPM), a neural formulation to encode target distributions for path guiding algorithms. We propose to use a continuous and compact neural implicit representation for encoding parametric models while decoding them via lightweight neural networks. We then derive a gradient-based optimization strategy to directly train the parameters of NPM with noisy Monte Carlo radiance estimates. Our approach efficiently models the target distribution (incident radiance or the product integrand) for path guiding, and outperforms previous guiding methods by capturing the spatio-directional correlations more accurately. Moreover, our approach is more training efficient and is practical for parallelization on modern GPUs.