# Rendering-Aware Sparse Sampling for BRDF Acquisition

arXiv: https://arxiv.org/abs/2604.26740 · HTML: https://mlsanity.com/paper/2604.26740/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 25 references that are in the map.
- First posted on arXiv 29 Apr 2026.

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

- [RGBleftrightarrowX: Image decomposition and synthesis using material- and lighting-aware diffusion models](https://mlsanity.com/paper/2405.00666/) (2024)
- [Flexible SVBRDF Capture with a Multi-Image Deep Network](https://mlsanity.com/paper/1906.11557/) (2019)
- [Real-Time Neural Appearance Models](https://mlsanity.com/paper/2305.02678/) (2023)
- [Learning to Learn and Sample BRDFs](https://mlsanity.com/paper/2210.03510/) (2022)
- [Hypernetworks for Generalizable BRDF Representation](https://mlsanity.com/paper/2311.15783/) (2023)
- [FROST-BRDF: A Fast and Robust Optimal Sampling Technique for BRDF Acquisition](https://mlsanity.com/paper/2401.07283/) (2024)

## Abstract (arXiv)

Accurate BRDF acquisition is essential for realistic rendering, but dense gonioreflectometer measurements are slow and expensive. We study how to select a small set of BRDF measurements that is most informative for reconstructing material appearance under a learned BRDF prior. Existing sparse-acquisition methods often optimize samples for BRDF-space reconstruction for all materials, while the perceptual importance of a adaptive measurement ultimately depends on its effect on each rendered appearance. We therefore formulate sparse adaptive acquisition as a rendering-aware optimization problem. Our method combines a set encoder for sparse coordinate--value observations, a pretrained hypernetwork-based/PCA-based BRDF reconstructor, and a differentiable renderer. During sampler training, the reconstructor remains fixed, and gradients from a rendered-image loss optimize the measurement locations. This separates acquisition design from prior fitting and encourages the sampler to choose directions that are informative under the learned material distribution. To make the comparison controlled, we evaluate the uniform baseline, meta-learning method, HyperBRDF method, and our learned sampler under matched sample numbers, train/test split, rendering scene, object mask, image mapping, and metrics. Our central claim: rendering-aware sampling improves extremely sparse BRDF acquisition when final rendered appearance is the target. BRDF-space and combined losses are reported only as ablations, together with joint refinement and image-only latent fitting for unseen materials.