# Materials for Masses: SVBRDF Acquisition with a Single Mobile Phone Image

arXiv: https://arxiv.org/abs/1804.05790 · HTML: https://mlsanity.com/paper/1804.05790/

## Key facts

- Cited by 66 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2018.

## Built on by (most cited first, 30 of 66)

- [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)
- [Unified Shape and SVBRDF Recovery using Differentiable Monte Carlo Rendering](https://mlsanity.com/paper/2103.15208/) (2021)
- [PS-NeRF: Neural Inverse Rendering for Multi-view Photometric Stereo](https://mlsanity.com/paper/2207.11406/) (2022)
- [Physically-Based Editing of Indoor Scene Lighting from a Single Image](https://mlsanity.com/paper/2205.09343/) (2022)
- [IRISformer: Dense Vision Transformers for Single-Image Inverse Rendering in Indoor Scenes](https://mlsanity.com/paper/2206.08423/) (2022)
- [Deep Polarization Imaging for 3D shape and SVBRDF Acquisition](https://mlsanity.com/paper/2105.02875/) (2021)
- [FlashTex: Fast Relightable Mesh Texturing with LightControlNet](https://mlsanity.com/paper/2402.13251/) (2024)
- [DIB-R++: Learning to Predict Lighting and Material with a Hybrid Differentiable Renderer](https://mlsanity.com/paper/2111.00140/) (2021)
- [Stanford-ORB: A Real-World 3D Object Inverse Rendering Benchmark](https://mlsanity.com/paper/2310.16044/) (2023)
- [AvatarMe++: Facial Shape and BRDF Inference with Photorealistic Rendering-Aware GANs](https://mlsanity.com/paper/2112.05957/) (2021)
- [MatFuse: Controllable Material Generation with Diffusion Models](https://mlsanity.com/paper/2308.11408/) (2023)
- [A Survey on Intrinsic Images: Delving Deep Into Lambert and Beyond](https://mlsanity.com/paper/2112.03842/) (2021)
- [Generative Modelling of BRDF Textures from Flash Images](https://mlsanity.com/paper/2102.11861/) (2021)
- [De-rendering 3D Objects in the Wild](https://mlsanity.com/paper/2201.02279/) (2022)
- [MatFusion: A Generative Diffusion Model for SVBRDF Capture](https://mlsanity.com/paper/2406.06539/) (2024)
- [MATLABER: Material-Aware Text-to-3D via LAtent BRDF auto-EncodeR](https://mlsanity.com/paper/2308.09278/) (2023)
- [Digital Twin Catalog: A Large-Scale Photorealistic 3D Object Digital Twin Dataset](https://mlsanity.com/paper/2504.08541/) (2025)
- [De-rendering the World's Revolutionary Artefacts](https://mlsanity.com/paper/2104.03954/) (2021)
- [TileGen: Tileable, Controllable Material Generation and Capture](https://mlsanity.com/paper/2206.05649/) (2022)
- [Materialistic: Selecting Similar Materials in Images](https://mlsanity.com/paper/2305.13291/) (2023)
- [SurfaceNet: Adversarial SVBRDF Estimation from a Single Image](https://mlsanity.com/paper/2107.11298/) (2021)
- [NeISF: Neural Incident Stokes Field for Geometry and Material Estimation](https://mlsanity.com/paper/2311.13187/) (2023)
- [PhotoScene: Photorealistic Material and Lighting Transfer for Indoor Scenes](https://mlsanity.com/paper/2207.00757/) (2022)
- [UrbanIR: Large-Scale Urban Scene Inverse Rendering from a Single Video](https://mlsanity.com/paper/2306.09349/) (2023)
- [IRIS: Inverse Rendering of Indoor Scenes from Low Dynamic Range Images](https://mlsanity.com/paper/2401.12977/) (2024)
- [SeamlessGAN: Self-Supervised Synthesis of Tileable Texture Maps](https://mlsanity.com/paper/2201.05120/) (2022)
- [PhotoMat: A Material Generator Learned from Single Flash Photos](https://mlsanity.com/paper/2305.12296/) (2023)
- [Objects With Lighting: A Real-World Dataset for Evaluating Reconstruction and Rendering for Object Relighting](https://mlsanity.com/paper/2401.09126/) (2024)
- [SHINOBI: Shape and Illumination using Neural Object Decomposition via BRDF Optimization In-the-wild](https://mlsanity.com/paper/2401.10171/) (2024)

## Abstract (arXiv)

We propose a material acquisition approach to recover the spatially-varying BRDF and normal map of a near-planar surface from a single image captured by a handheld mobile phone camera. Our method images the surface under arbitrary environment lighting with the flash turned on, thereby avoiding shadows while simultaneously capturing high-frequency specular highlights. We train a CNN to regress an SVBRDF and surface normals from this image. Our network is trained using a large-scale SVBRDF dataset and designed to incorporate physical insights for material estimation, including an in-network rendering layer to model appearance and a material classifier to provide additional supervision during training. We refine the results from the network using a dense CRF module whose terms are designed specifically for our task. The framework is trained end-to-end and produces high quality results for a variety of materials. We provide extensive ablation studies to evaluate our network on both synthetic and real data, while demonstrating significant improvements in comparisons with prior works.