# Efficient Reflectance Capture with a Deep Gated Mixture-of-Experts

arXiv: https://arxiv.org/abs/2203.15258 · HTML: https://mlsanity.com/paper/2203.15258/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 42 references that are in the map.
- First posted on arXiv 29 Mar 2022.

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

- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [Scaling Vision with Sparse Mixture of Experts](https://mlsanity.com/paper/2106.05974/) (2021)
- [Single-Image SVBRDF Capture with a Rendering-Aware Deep Network](https://mlsanity.com/paper/1810.09718/) (2018)
- [Modeling Surface Appearance from a Single Photograph using Self-augmented Convolutional Neural Networks](https://mlsanity.com/paper/1809.00886/) (2018)
- [Flexible SVBRDF Capture with a Multi-Image Deep Network](https://mlsanity.com/paper/1906.11557/) (2019)
- [Generative Modelling of BRDF Textures from Flash Images](https://mlsanity.com/paper/2102.11861/) (2021)

## Built on by (most cited first, 3 of 3)

- [HumanMaterial: Human Material Estimation from a Single Image via Progressive Training](https://mlsanity.com/paper/2507.18385/) (2025)
- [Differentiable Adaptive 4D Structured Illumination for Joint Capture of Shape and Reflectance](https://mlsanity.com/paper/2605.06214/) (2026)
- [HAFMat: Hybrid Priors Guided Adaptive Fusion for Single-Image Human Material Estimation](https://mlsanity.com/paper/2606.16323/) (2026)

## Abstract (arXiv)

We present a novel framework to efficiently acquire near-planar anisotropic reflectance in a pixel-independent fashion, using a deep gated mixtureof-experts. While existing work employs a unified network to handle all possible input, our network automatically learns to condition on the input for enhanced reconstruction. We train a gating module to select one out of a number of specialized decoders for reflectance reconstruction, based on photometric measurements, essentially trading generality for quality. A common, pre-trained latent transform module is also appended to each decoder, to offset the burden of the increased number of decoders. In addition, the illumination conditions during acquisition can be jointly optimized. The effectiveness of our framework is validated on a wide variety of challenging samples using a near-field lightstage. Compared with the state-of-the-art technique, our results are improved at the same input bandwidth, and our bandwidth can be reduced to about 1/3 for equal-quality results.