# Learning to Learn and Sample BRDFs

arXiv: https://arxiv.org/abs/2210.03510 · HTML: https://mlsanity.com/paper/2210.03510/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 65 references that are in the map.
- First posted on arXiv 7 Oct 2022.

## Builds on (most cited first, 29 of 29)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Variational Inference with Normalizing Flows](https://mlsanity.com/paper/1505.05770/) (2015)
- [Meta-SGD: Learning to Learn Quickly for Few-Shot Learning](https://mlsanity.com/paper/1707.09835/) (2017)
- [Light Field Networks: Neural Scene Representations with Single-Evaluation Rendering](https://mlsanity.com/paper/2106.02634/) (2021)
- [Not All Samples Are Created Equal: Deep Learning with Importance Sampling](https://mlsanity.com/paper/1803.00942/) (2018)
- [Learned Initializations for Optimizing Coordinate-Based Neural Representations](https://mlsanity.com/paper/2012.02189/) (2020)
- [MetaSDF: Meta-learning Signed Distance Functions](https://mlsanity.com/paper/2006.09662/) (2020)
- [Stochastic Gradient Descent, Weighted Sampling, and the Randomized Kaczmarz algorithm](https://mlsanity.com/paper/1310.5715/) (2013)
- [Single-Image SVBRDF Capture with a Rendering-Aware Deep Network](https://mlsanity.com/paper/1810.09718/) (2018)
- [Stochastic Optimization with Importance Sampling](https://mlsanity.com/paper/1401.2753/) (2014)
- [Real-time Neural Radiance Caching for Path Tracing](https://mlsanity.com/paper/2106.12372/) (2021)
- [MetaAvatar: Learning Animatable Clothed Human Models from Few Depth Images](https://mlsanity.com/paper/2106.11944/) (2021)
- [learn2learn: A Library for Meta-Learning Research](https://mlsanity.com/paper/2008.12284/) (2020)
- [Generative Modelling of BRDF Textures from Flash Images](https://mlsanity.com/paper/2102.11861/) (2021)
- [MaterialGAN: Reflectance Capture using a Generative SVBRDF Model](https://mlsanity.com/paper/2010.00114/) (2020)
- [Fast Training of Neural Lumigraph Representations using Meta Learning](https://mlsanity.com/paper/2106.14942/) (2021)
- [Neural BRDF Representation and Importance Sampling](https://mlsanity.com/paper/2102.05963/) (2021)
- [Guided Fine-Tuning for Large-Scale Material Transfer](https://mlsanity.com/paper/2007.03059/) (2020)
- [Seeing the World in a Bag of Chips](https://mlsanity.com/paper/2001.04642/) (2020)
- [Safe Adaptive Importance Sampling](https://mlsanity.com/paper/1711.02637/) (2017)
- [Material Editing Using a Physically Based Rendering Network](https://mlsanity.com/paper/1708.00106/) (2017)
- [Deep Reflectance Maps](https://mlsanity.com/paper/1511.04384/) (2015)
- [Active Exploration for Neural Global Illumination of Variable Scenes](https://mlsanity.com/paper/2203.08272/) (2022)
- [Online Learning to Sample](https://mlsanity.com/paper/1506.09016/) (2015)
- [Metappearance: Meta-Learning for Visual Appearance Reproduction](https://mlsanity.com/paper/2204.08993/) (2022)
- [Deep Appearance Maps](https://mlsanity.com/paper/1804.00863/) (2018)
- [Neural BRDFs: Representation and Operations](https://mlsanity.com/paper/2111.03797/) (2021)

## Built on by (most cited first, 9 of 9)

- [Improving NeRF Quality by Progressive Camera Placement for Unrestricted Navigation in Complex Environments](https://mlsanity.com/paper/2309.00014/) (2023)
- [Neural Semantic Surface Maps](https://mlsanity.com/paper/2309.04836/) (2023)
- [NeRF Analogies: Example-Based Visual Attribute Transfer for NeRFs](https://mlsanity.com/paper/2402.08622/) (2024)
- [Plateau-reduced Differentiable Path Tracing](https://mlsanity.com/paper/2211.17263/) (2022)
- [Physically Based Neural Bidirectional Reflectance Distribution Function](https://mlsanity.com/paper/2411.02347/) (2024)
- [Neural Bounding](https://mlsanity.com/paper/2310.06822/) (2023)
- [Deep image-based Adaptive BRDF Measure](https://mlsanity.com/paper/2410.02917/) (2024)
- [Rendering-Aware Sparse Sampling for BRDF Acquisition](https://mlsanity.com/paper/2604.26740/) (2026)
- [Differentiable Adaptive 4D Structured Illumination for Joint Capture of Shape and Reflectance](https://mlsanity.com/paper/2605.06214/) (2026)

## Abstract (arXiv)

We propose a method to accelerate the joint process of physically acquiring and learning neural Bi-directional Reflectance Distribution Function (BRDF) models. While BRDF learning alone can be accelerated by meta-learning, acquisition remains slow as it relies on a mechanical process. We show that meta-learning can be extended to optimize the physical sampling pattern, too. After our method has been meta-trained for a set of fully-sampled BRDFs, it is able to quickly train on new BRDFs with up to five orders of magnitude fewer physical acquisition samples at similar quality. Our approach also extends to other linear and non-linear BRDF models, which we show in an extensive evaluation.