# Hypernetworks for Generalizable BRDF Representation

arXiv: https://arxiv.org/abs/2311.15783 · HTML: https://mlsanity.com/paper/2311.15783/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2023.

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

- [Lunar-G2R: Geometry-to-Reflectance Learning for High-Fidelity Lunar BRDF Estimation](https://mlsanity.com/paper/2601.10449/) (2026)
- [FreNBRDF: A Frequency-Rectified Neural Material Representation](https://mlsanity.com/paper/2507.00476/) (2025)
- [SpecGen: Neural Spectral BRDF Generation via Spectral-Spatial Tri-plane Aggregation](https://mlsanity.com/paper/2508.17316/) (2025)
- [Rendering-Aware Sparse Sampling for BRDF Acquisition](https://mlsanity.com/paper/2604.26740/) (2026)
- [Neural Texture Compression using Hypernetworks](https://mlsanity.com/paper/2606.26913/) (2026)
- [A Hybrid Neural-Microfacet BRDF Model for Real-Time Rendering](https://mlsanity.com/paper/2608.09604/) (2026)
- [RenderFormer-V2: Neural Rendering with Heterogeneous Scene Primitives](https://mlsanity.com/paper/2609.05738/) (2026)

## Abstract (arXiv)

In this paper, we introduce a technique to estimate measured BRDFs from a sparse set of samples. Our approach offers accurate BRDF reconstructions that are generalizable to new materials. This opens the door to BDRF reconstructions from a variety of data sources. The success of our approach relies on the ability of hypernetworks to generate a robust representation of BRDFs and a set encoder that allows us to feed inputs of different sizes to the architecture. The set encoder and the hypernetwork also enable the compression of densely sampled BRDFs. We evaluate our technique both qualitatively and quantitatively on the well-known MERL dataset of 100 isotropic materials. Our approach accurately 1) estimates the BRDFs of unseen materials even for an extremely sparse sampling, 2) compresses the measured BRDFs into very small embeddings, e.g., 7D.