# Neural Active Structure-from-Motion in Dark and Textureless Environment

arXiv: https://arxiv.org/abs/2410.15378 · HTML: https://mlsanity.com/paper/2410.15378/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 46 references that are in the map.
- First posted on arXiv 20 Oct 2024.

## Builds on (most cited first, 21 of 21)

- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction](https://mlsanity.com/paper/2106.10689/) (2021)
- [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019)
- [Volume Rendering of Neural Implicit Surfaces](https://mlsanity.com/paper/2106.12052/) (2021)
- [Unsupervised Learning of Depth and Ego-Motion from Video](https://mlsanity.com/paper/1704.07813/) (2017)
- [Implicit Geometric Regularization for Learning Shapes](https://mlsanity.com/paper/2002.10099/) (2020)
- [BARF: Bundle-Adjusting Neural Radiance Fields](https://mlsanity.com/paper/2104.06405/) (2021)
- [BlendedMVS: A Large-scale Dataset for Generalized Multi-view Stereo Networks](https://mlsanity.com/paper/1911.10127/) (2019)
- [Neuralangelo: High-Fidelity Neural Surface Reconstruction](https://mlsanity.com/paper/2306.03092/) (2023)
- [NeRF in the Dark: High Dynamic Range View Synthesis from Noisy Raw Images](https://mlsanity.com/paper/2111.13679/) (2021)
- [NeuS2: Fast Learning of Neural Implicit Surfaces for Multi-view Reconstruction](https://mlsanity.com/paper/2212.05231/) (2022)
- [Self-Calibrating Neural Radiance Fields](https://mlsanity.com/paper/2108.13826/) (2021)
- [DeepV2D: Video to Depth with Differentiable Structure from Motion](https://mlsanity.com/paper/1812.04605/) (2018)
- [HDR-NeRF: High Dynamic Range Neural Radiance Fields](https://mlsanity.com/paper/2111.14451/) (2021)
- [NeRF-SR: High-Quality Neural Radiance Fields using Supersampling](https://mlsanity.com/paper/2112.01759/) (2021)
- [CamP: Camera Preconditioning for Neural Radiance Fields](https://mlsanity.com/paper/2308.10902/) (2023)
- [NeRFMeshing: Distilling Neural Radiance Fields into Geometrically-Accurate 3D Meshes](https://mlsanity.com/paper/2303.09431/) (2023)
- [Lighting up NeRF via Unsupervised Decomposition and Enhancement](https://mlsanity.com/paper/2307.10664/) (2023)
- [Pose-Free Neural Radiance Fields via Implicit Pose Regularization](https://mlsanity.com/paper/2308.15049/) (2023)
- [3D Neural Sculpting (3DNS): Editing Neural Signed Distance Functions](https://mlsanity.com/paper/2209.13971/) (2022)
- [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 Inverse Rendering from Propagating Light](https://mlsanity.com/paper/2506.05347/) (2025)

## Abstract (arXiv)

Active 3D measurement, especially structured light (SL) has been widely used in various fields for its robustness against textureless or equivalent surfaces by low light illumination. In addition, reconstruction of large scenes by moving the SL system has become popular, however, there have been few practical techniques to obtain the system's precise pose information only from images, since most conventional techniques are based on image features, which cannot be retrieved under textureless environments. In this paper, we propose a simultaneous shape reconstruction and pose estimation technique for SL systems from an image set where sparsely projected patterns onto the scene are observed (i.e. no scene texture information), which we call Active SfM. To achieve this, we propose a full optimization framework of the volumetric shape that employs neural signed distance fields (Neural-SDF) for SL with the goal of not only reconstructing the scene shape but also estimating the poses for each motion of the system. Experimental results show that the proposed method is able to achieve accurate shape reconstruction as well as pose estimation from images where only projected patterns are observed.