Robust Single-shot Structured Light 3D Imaging via Neural Feature Decoding

• URL: http://arxiv.org/abs/2512.14028v1
• Date: Tue, 16 Dec 2025 02:47:38 GMT
• Title: Robust Single-shot Structured Light 3D Imaging via Neural Feature Decoding
• Authors: Jiaheng Li, Qiyu Dai, Lihan Li, Praneeth Chakravarthula, He Sun, Baoquan Chen, Wenzheng Chen,
• Abstract summary: We consider the problem of active 3D imaging using single-shot structured light systems.<n>Traditional structured light methods typically decode depth correspondences through pixel-domain matching algorithms.<n>Inspired by recent advances in neural feature matching, we propose a learning-based structured light decoding framework.
• Score: 33.013553875034795
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We consider the problem of active 3D imaging using single-shot structured light systems, which are widely employed in commercial 3D sensing devices such as Apple Face ID and Intel RealSense. Traditional structured light methods typically decode depth correspondences through pixel-domain matching algorithms, resulting in limited robustness under challenging scenarios like occlusions, fine-structured details, and non-Lambertian surfaces. Inspired by recent advances in neural feature matching, we propose a learning-based structured light decoding framework that performs robust correspondence matching within feature space rather than the fragile pixel domain. Our method extracts neural features from the projected patterns and captured infrared (IR) images, explicitly incorporating their geometric priors by building cost volumes in feature space, achieving substantial performance improvements over pixel-domain decoding approaches. To further enhance depth quality, we introduce a depth refinement module that leverages strong priors from large-scale monocular depth estimation models, improving fine detail recovery and global structural coherence. To facilitate effective learning, we develop a physically-based structured light rendering pipeline, generating nearly one million synthetic pattern-image pairs with diverse objects and materials for indoor settings. Experiments demonstrate that our method, trained exclusively on synthetic data with multiple structured light patterns, generalizes well to real-world indoor environments, effectively processes various pattern types without retraining, and consistently outperforms both commercial structured light systems and passive stereo RGB-based depth estimation methods. Project page: https://namisntimpot.github.io/NSLweb/.

Related papers

• IDArb: Intrinsic Decomposition for Arbitrary Number of Input Views and Illuminations [64.07859467542664]
  Capturing geometric and material information from images remains a fundamental challenge in computer vision and graphics.<n>Traditional optimization-based methods often require hours of computational time to reconstruct geometry, material properties, and environmental lighting from dense multi-view inputs.<n>We introduce IDArb, a diffusion-based model designed to perform intrinsic decomposition on an arbitrary number of images under varying illuminations.
  arXiv  Detail & Related papers  (2024-12-16T18:52:56Z)
• PBIR-NIE: Glossy Object Capture under Non-Distant Lighting [30.325872237020395]
  Glossy objects present a significant challenge for 3D reconstruction from multi-view input images under natural lighting. We introduce PBIR-NIE, an inverse rendering framework designed to holistically capture the geometry, material attributes, and surrounding illumination of such objects.
  arXiv  Detail & Related papers  (2024-08-13T13:26:24Z)
• Depth Reconstruction with Neural Signed Distance Fields in Structured Light Systems [15.603880588503355]
  We introduce a novel depth estimation technique for multi-frame structured light setups using neural implicit representations of 3D space. Our approach employs a neural signed distance field (SDF), trained through self-supervised differentiable rendering.
  arXiv  Detail & Related papers  (2024-05-20T13:24:35Z)
• Holistic Inverse Rendering of Complex Facade via Aerial 3D Scanning [38.72679977945778]
  We use multi-view aerial images to reconstruct the geometry, lighting, and material of facades using neural signed distance fields (SDFs) The experiment demonstrates the superior quality of our method on facade holistic inverse rendering, novel view synthesis, and scene editing compared to state-of-the-art baselines.
  arXiv  Detail & Related papers  (2023-11-20T15:03:56Z)
• Multi-View Neural Surface Reconstruction with Structured Light [7.709526244898887]
  Three-dimensional (3D) object reconstruction based on differentiable rendering (DR) is an active research topic in computer vision. 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. Our method realizes high reconstruction accuracy in the textureless region and reduces efforts for camera pose calibration.
  arXiv  Detail & Related papers  (2022-11-22T03:10:46Z)
• Neural 3D Reconstruction in the Wild [86.6264706256377]
  We introduce a new method that enables efficient and accurate surface reconstruction from Internet photo collections. We present a new benchmark and protocol for evaluating reconstruction performance on such in-the-wild scenes.
  arXiv  Detail & Related papers  (2022-05-25T17:59:53Z)
• Learning Deep Context-Sensitive Decomposition for Low-Light Image Enhancement [58.72667941107544]
  A typical framework is to simultaneously estimate the illumination and reflectance, but they disregard the scene-level contextual information encapsulated in feature spaces. We develop a new context-sensitive decomposition network architecture to exploit the scene-level contextual dependencies on spatial scales. We develop a lightweight CSDNet (named LiteCSDNet) by reducing the number of channels.
  arXiv  Detail & Related papers  (2021-12-09T06:25:30Z)
• Extracting Triangular 3D Models, Materials, and Lighting From Images [59.33666140713829]
  We present an efficient method for joint optimization of materials and lighting from multi-view image observations. We leverage meshes with spatially-varying materials and environment that can be deployed in any traditional graphics engine.
  arXiv  Detail & Related papers  (2021-11-24T13:58:20Z)
• Polka Lines: Learning Structured Illumination and Reconstruction for Active Stereo [52.68109922159688]
  We introduce a novel differentiable image formation model for active stereo, relying on both wave and geometric optics, and a novel trinocular reconstruction network. The jointly optimized pattern, which we dub "Polka Lines," together with the reconstruction network, achieve state-of-the-art active-stereo depth estimates across imaging conditions.
  arXiv  Detail & Related papers  (2020-11-26T04:02:43Z)
• Deep 3D Capture: Geometry and Reflectance from Sparse Multi-View Images [59.906948203578544]
  We introduce a novel learning-based method to reconstruct the high-quality geometry and complex, spatially-varying BRDF of an arbitrary object. We first estimate per-view depth maps using a deep multi-view stereo network. These depth maps are used to coarsely align the different views. We propose a novel multi-view reflectance estimation network architecture.
  arXiv  Detail & Related papers  (2020-03-27T21:28:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.