# Flexible SVBRDF Capture with a Multi-Image Deep Network

arXiv: https://arxiv.org/abs/1906.11557 · HTML: https://mlsanity.com/paper/1906.11557/

## Key facts

- Cited by 50 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2019.

## Built on by (most cited first, 30 of 50)

- [ABO: Dataset and Benchmarks for Real-World 3D Object Understanding](https://mlsanity.com/paper/2110.06199/) (2021)
- [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)
- [Deep Polarization Imaging for 3D shape and SVBRDF Acquisition](https://mlsanity.com/paper/2105.02875/) (2021)
- [ControlMat: A Controlled Generative Approach to Material Capture](https://mlsanity.com/paper/2309.01700/) (2023)
- [MatSynth: A Modern PBR Materials Dataset](https://mlsanity.com/paper/2401.06056/) (2024)
- [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)
- [MatFusion: A Generative Diffusion Model for SVBRDF Capture](https://mlsanity.com/paper/2406.06539/) (2024)
- [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)
- [Multi-view Inverse Rendering for Large-scale Real-world Indoor Scenes](https://mlsanity.com/paper/2211.10206/) (2022)
- [ZeST: Zero-Shot Material Transfer from a Single Image](https://mlsanity.com/paper/2404.06425/) (2024)
- [PhotoScene: Photorealistic Material and Lighting Transfer for Indoor Scenes](https://mlsanity.com/paper/2207.00757/) (2022)
- [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)
- [SHINOBI: Shape and Illumination using Neural Object Decomposition via BRDF Optimization In-the-wild](https://mlsanity.com/paper/2401.10171/) (2024)
- [SAMa: Material-aware 3D Selection and Segmentation](https://mlsanity.com/paper/2411.19322/) (2024)
- [MaterialPicker: Multi-Modal DiT-Based Material Generation](https://mlsanity.com/paper/2412.03225/) (2024)
- [MARBLE: Material Recomposition and Blending in CLIP-Space](https://mlsanity.com/paper/2506.05313/) (2025)
- [VQ-NeRF: Neural Reflectance Decomposition and Editing with Vector Quantization](https://mlsanity.com/paper/2310.11864/) (2023)
- [SViM3D: Stable Video Material Diffusion for Single Image 3D Generation](https://mlsanity.com/paper/2510.08271/) (2025)
- [Neural Photometry-guided Visual Attribute Transfer](https://mlsanity.com/paper/2112.02520/) (2021)
- [SVBRDF Recovery From a Single Image With Highlights using a Pretrained Generative Adversarial Network](https://mlsanity.com/paper/2111.00943/) (2021)
- [KiloNeuS: A Versatile Neural Implicit Surface Representation for Real-Time Rendering](https://mlsanity.com/paper/2206.10885/) (2022)
- [JoIN: Joint GANs Inversion for Intrinsic Image Decomposition](https://mlsanity.com/paper/2305.11321/) (2023)
- [StableMaterials: Enhancing Diversity in Material Generation via Semi-Supervised Learning](https://mlsanity.com/paper/2406.09293/) (2024)

## Abstract (arXiv)

Empowered by deep learning, recent methods for material capture can estimate a spatially-varying reflectance from a single photograph. Such lightweight capture is in stark contrast with the tens or hundreds of pictures required by traditional optimization-based approaches. However, a single image is often simply not enough to observe the rich appearance of real-world materials. We present a deep-learning method capable of estimating material appearance from a variable number of uncalibrated and unordered pictures captured with a handheld camera and flash. Thanks to an order-independent fusing layer, this architecture extracts the most useful information from each picture, while benefiting from strong priors learned from data. The method can handle both view and light direction variation without calibration. We show how our method improves its prediction with the number of input pictures, and reaches high quality reconstructions with as little as 1 to 10 images -- a sweet spot between existing single-image and complex multi-image approaches.