# ActiveNeuS: Neural Signed Distance Fields for Active Stereo

arXiv: https://arxiv.org/abs/2410.15376 · HTML: https://mlsanity.com/paper/2410.15376/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 38 references that are in the map.
- First posted on arXiv 20 Oct 2024.

## Builds on (most cited first, 10 of 10)

- [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)
- [Depth-supervised NeRF: Fewer Views and Faster Training for Free](https://mlsanity.com/paper/2107.02791/) (2021)
- [Implicit Geometric Regularization for Learning Shapes](https://mlsanity.com/paper/2002.10099/) (2020)
- [BlendedMVS: A Large-scale Dataset for Generalized Multi-view Stereo Networks](https://mlsanity.com/paper/1911.10127/) (2019)
- [NeRD: Neural Reflectance Decomposition from Image Collections](https://mlsanity.com/paper/2012.03918/) (2020)
- [NeRF-SR: High-Quality Neural Radiance Fields using Supersampling](https://mlsanity.com/paper/2112.01759/) (2021)
- [Polka Lines: Learning Structured Illumination and Reconstruction for Active Stereo](https://mlsanity.com/paper/2011.13117/) (2020)
- [Physics-based Differentiable Depth Sensor Simulation](https://mlsanity.com/paper/2103.16563/) (2021)
- [Multi-View Neural Surface Reconstruction with Structured Light](https://mlsanity.com/paper/2211.11971/) (2022)

## Built on by (most cited first, 1 of 1)

- [Neural shape reconstruction from multiple views with static pattern projection](https://mlsanity.com/paper/2506.01389/) (2025)

## Abstract (arXiv)

3D-shape reconstruction in extreme environments, such as low illumination or scattering condition, has been an open problem and intensively researched. Active stereo is one of potential solution for such environments for its robustness and high accuracy. However, active stereo systems usually consist of specialized system configurations with complicated algorithms, which narrow their application. In this paper, we propose Neural Signed Distance Field for active stereo systems to enable implicit correspondence search and triangulation in generalized Structured Light. With our technique, textureless or equivalent surfaces by low light condition are successfully reconstructed even with a small number of captured images. Experiments were conducted to confirm that the proposed method could achieve state-of-the-art reconstruction quality under such severe condition. We also demonstrated that the proposed method worked in an underwater scenario.