# Deep SVBRDF Estimation on Real Materials

arXiv: https://arxiv.org/abs/2010.04143 · HTML: https://mlsanity.com/paper/2010.04143/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Oct 2020.

## Built on by (most cited first, 15 of 15)

- [Neural-PIL: Neural Pre-Integrated Lighting for Reflectance Decomposition](https://mlsanity.com/paper/2110.14373/) (2021)
- [SAMURAI: Shape And Material from Unconstrained Real-world Arbitrary Image collections](https://mlsanity.com/paper/2205.15768/) (2022)
- [AvatarMe++: Facial Shape and BRDF Inference with Photorealistic Rendering-Aware GANs](https://mlsanity.com/paper/2112.05957/) (2021)
- [NeMF: Inverse Volume Rendering with Neural Microflake Field](https://mlsanity.com/paper/2304.00782/) (2023)
- [Material Palette: Extraction of Materials from a Single Image](https://mlsanity.com/paper/2311.17060/) (2023)
- [Objects With Lighting: A Real-World Dataset for Evaluating Reconstruction and Rendering for Object Relighting](https://mlsanity.com/paper/2401.09126/) (2024)
- [SViM3D: Stable Video Material Diffusion for Single Image 3D Generation](https://mlsanity.com/paper/2510.08271/) (2025)
- [NeuS-PIR: Learning Relightable Neural Surface using Pre-Integrated Rendering](https://mlsanity.com/paper/2306.07632/) (2023)
- [MaterialSeg3D: Segmenting Dense Materials from 2D Priors for 3D Assets](https://mlsanity.com/paper/2404.13923/) (2024)
- [NDJIR: Neural Direct and Joint Inverse Rendering for Geometry, Lights, and Materials of Real Object](https://mlsanity.com/paper/2302.00675/) (2023)
- [Deep scene-scale material estimation from multi-view indoor captures](https://mlsanity.com/paper/2211.08047/) (2022)
- [Incorporating dense metric depth into neural 3D representations for view synthesis and relighting](https://mlsanity.com/paper/2409.03061/) (2024)
- [MatE: Material Extraction from Single-Image via Geometric Prior](https://mlsanity.com/paper/2512.18312/) (2025)
- [DealMaTe: Multi-Dimensional Material Transfer via Diffusion Transformer](https://mlsanity.com/paper/2605.15681/) (2026)
- [From Unity Simulation to Diffusion-Based Augmentation: Quantifying Dataset Balance for Robust Object Detection](https://mlsanity.com/paper/2609.38010/) (2026)

## Abstract (arXiv)

Recent work has demonstrated that deep learning approaches can successfully be used to recover accurate estimates of the spatially-varying BRDF (SVBRDF) of a surface from as little as a single image. Closer inspection reveals, however, that most approaches in the literature are trained purely on synthetic data, which, while diverse and realistic, is often not representative of the richness of the real world. In this paper, we show that training such networks exclusively on synthetic data is insufficient to achieve adequate results when tested on real data. Our analysis leverages a new dataset of real materials obtained with a novel portable multi-light capture apparatus. Through an extensive series of experiments and with the use of a novel deep learning architecture, we explore two strategies for improving results on real data: finetuning, and a per-material optimization procedure. We show that adapting network weights to real data is of critical importance, resulting in an approach which significantly outperforms previous methods for SVBRDF estimation on real materials. Dataset and code are available at https://lvsn.github.io/real-svbrdf