# Deep image-based Adaptive BRDF Measure

arXiv: https://arxiv.org/abs/2410.02917 · HTML: https://mlsanity.com/paper/2410.02917/

## 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 3 Oct 2024.

## 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)
- [Neural Importance Sampling](https://mlsanity.com/paper/1808.03856/) (2018)
- [Flexible SVBRDF Capture with a Multi-Image Deep Network](https://mlsanity.com/paper/1906.11557/) (2019)
- [Learning to Learn and Sample BRDFs](https://mlsanity.com/paper/2210.03510/) (2022)
- [FROST-BRDF: A Fast and Robust Optimal Sampling Technique for BRDF Acquisition](https://mlsanity.com/paper/2401.07283/) (2024)
- [BSDF Importance Baking: A Lightweight Neural Solution to Importance Sampling General Parametric BSDFs](https://mlsanity.com/paper/2210.13681/) (2022)

## Abstract (arXiv)

Efficient and accurate measurement of the bi-directional reflectance distribution function (BRDF) plays a key role in high quality image rendering and physically accurate sensor simulation. However, obtaining the reflectance properties of a material is both time-consuming and challenging. This paper presents a novel method for minimizing the number of samples required for high quality BRDF capture using a gonio-reflectometer setup. Taking an image of the physical material sample as input a lightweight neural network first estimates the parameters of an analytic BRDF model, and the distribution of the sample locations. In a second step we use an image based loss to find the number of samples required to meet the accuracy required. This approach significantly accelerates the measurement process while maintaining a high level of accuracy and fidelity in the BRDF representation.