# Multi-view 3D Reconstruction of a Texture-less Smooth Surface of Unknown Generic Reflectance

arXiv: https://arxiv.org/abs/2105.11599 · HTML: https://mlsanity.com/paper/2105.11599/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 41 references that are in the map.
- First posted on arXiv 25 May 2021.

## Builds on (most cited first, 3 of 3)

- [Materials for Masses: SVBRDF Acquisition with a Single Mobile Phone Image](https://mlsanity.com/paper/1804.05790/) (2018)
- [Multi-View Photometric Stereo: A Robust Solution and Benchmark Dataset for Spatially Varying Isotropic Materials](https://mlsanity.com/paper/2001.06659/) (2020)
- [A Differential Volumetric Approach to Multi-View Photometric Stereo](https://mlsanity.com/paper/1811.01984/) (2018)

## Built on by (most cited first, 17 of 17)

- [NeRO: Neural Geometry and BRDF Reconstruction of Reflective Objects from Multiview Images](https://mlsanity.com/paper/2305.17398/) (2023)
- [PS-NeRF: Neural Inverse Rendering for Multi-view Photometric Stereo](https://mlsanity.com/paper/2207.11406/) (2022)
- [Ref-NeuS: Ambiguity-Reduced Neural Implicit Surface Learning for Multi-View Reconstruction with Reflection](https://mlsanity.com/paper/2303.10840/) (2023)
- [DreamMat: High-quality PBR Material Generation with Geometry- and Light-aware Diffusion Models](https://mlsanity.com/paper/2405.17176/) (2024)
- [WildLight: In-the-wild Inverse Rendering with a Flashlight](https://mlsanity.com/paper/2303.14190/) (2023)
- [Looking Through the Glass: Neural Surface Reconstruction Against High Specular Reflections](https://mlsanity.com/paper/2304.08706/) (2023)
- [SIRe-IR: Inverse Rendering for BRDF Reconstruction with Shadow and Illumination Removal in High-Illuminance Scenes](https://mlsanity.com/paper/2310.13030/) (2023)
- [Towards 3D Vision with Low-Cost Single-Photon Cameras](https://mlsanity.com/paper/2403.17801/) (2024)
- [Balanced 3DGS: Gaussian-wise Parallelism Rendering with Fine-Grained Tiling](https://mlsanity.com/paper/2412.17378/) (2024)
- [nLMVS-Net: Deep Non-Lambertian Multi-View Stereo](https://mlsanity.com/paper/2207.11876/) (2022)
- [SFDM: Robust Decomposition of Geometry and Reflectance for Realistic Face Rendering from Sparse-view Images](https://mlsanity.com/paper/2312.06085/) (2023)
- [Towards Scalable Multi-View Reconstruction of Geometry and Materials](https://mlsanity.com/paper/2306.03747/) (2023)
- [Free-Moving Object Reconstruction and Pose Estimation with Virtual Camera](https://mlsanity.com/paper/2405.05858/) (2024)
- [PIR: Photometric Inverse Rendering with Shading Cues Modeling and Surface Reflectance Regularization](https://mlsanity.com/paper/2408.06828/) (2024)
- [DeepShaRM: Multi-View Shape and Reflectance Map Recovery Under Unknown Lighting](https://mlsanity.com/paper/2310.17632/) (2023)
- [Shape2.5D: A Dataset of Texture-less Surfaces for Depth and Normals Estimation](https://mlsanity.com/paper/2406.15831/) (2024)
- [Spatial Polarization Multiplexing: Single-Shot Invisible Shape and Reflectance Recovery](https://mlsanity.com/paper/2504.13177/) (2025)

## Abstract (arXiv)

Recovering the 3D geometry of a purely texture-less object with generally unknown surface reflectance (e.g. non-Lambertian) is regarded as a challenging task in multi-view reconstruction. The major obstacle revolves around establishing cross-view correspondences where photometric constancy is violated. This paper proposes a simple and practical solution to overcome this challenge based on a co-located camera-light scanner device. Unlike existing solutions, we do not explicitly solve for correspondence. Instead, we argue the problem is generally well-posed by multi-view geometrical and photometric constraints, and can be solved from a small number of input views. We formulate the reconstruction task as a joint energy minimization over the surface geometry and reflectance. Despite this energy is highly non-convex, we develop an optimization algorithm that robustly recovers globally optimal shape and reflectance even from a random initialization. Extensive experiments on both simulated and real data have validated our method, and possible future extensions are discussed.