# Multi-View Neural Surface Reconstruction with Structured Light

arXiv: https://arxiv.org/abs/2211.11971 · HTML: https://mlsanity.com/paper/2211.11971/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 35 references that are in the map.
- First posted on arXiv 22 Nov 2022.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction](https://mlsanity.com/paper/2106.10689/) (2021)
- [Volume Rendering of Neural Implicit Surfaces](https://mlsanity.com/paper/2106.12052/) (2021)
- [Implicit Geometric Regularization for Learning Shapes](https://mlsanity.com/paper/2002.10099/) (2020)
- [Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision](https://mlsanity.com/paper/1912.07372/) (2019)
- [Neural Fields in Visual Computing and Beyond](https://mlsanity.com/paper/2111.11426/) (2021)
- [Differentiable Rendering: A Survey](https://mlsanity.com/paper/2006.12057/) (2020)
- [Pro-Cam SSfM: Projector-Camera System for Structure and Spectral Reflectance from Motion](https://mlsanity.com/paper/1908.08185/) (2019)
- [3DUNDERWORLD-SLS: An Open-Source Structured-Light Scanning System for Rapid Geometry Acquisition](https://mlsanity.com/paper/1406.6595/) (2014)

## Built on by (most cited first, 3 of 3)

- [ActiveNeuS: Neural Signed Distance Fields for Active Stereo](https://mlsanity.com/paper/2410.15376/) (2024)
- [Neural Active Structure-from-Motion in Dark and Textureless Environment](https://mlsanity.com/paper/2410.15378/) (2024)
- [Neural shape reconstruction from multiple views with static pattern projection](https://mlsanity.com/paper/2506.01389/) (2025)

## Abstract (arXiv)

Three-dimensional (3D) object reconstruction based on differentiable rendering (DR) is an active research topic in computer vision. DR-based methods minimize the difference between the rendered and target images by optimizing both the shape and appearance and realizing a high visual reproductivity. However, most approaches perform poorly for textureless objects because of the geometrical ambiguity, which means that multiple shapes can have the same rendered result in such objects. To overcome this problem, we introduce active sensing with structured light (SL) into multi-view 3D object reconstruction based on DR to learn the unknown geometry and appearance of arbitrary scenes and camera poses. More specifically, our framework leverages the correspondences between pixels in different views calculated by structured light as an additional constraint in the DR-based optimization of implicit surface, color representations, and camera poses. Because camera poses can be optimized simultaneously, our method realizes high reconstruction accuracy in the textureless region and reduces efforts for camera pose calibration, which is required for conventional SL-based methods. Experiment results on both synthetic and real data demonstrate that our system outperforms conventional DR- and SL-based methods in a high-quality surface reconstruction, particularly for challenging objects with textureless or shiny surfaces.