Multi-View Neural Surface Reconstruction with Structured Light

• URL: http://arxiv.org/abs/2211.11971v1
• Date: Tue, 22 Nov 2022 03:10:46 GMT
• Title: Multi-View Neural Surface Reconstruction with Structured Light
• Authors: Chunyu Li, Taisuke Hashimoto, Eiichi Matsumoto, Hiroharu Kato
• Abstract summary: 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.
• Score: 7.709526244898887
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• UniSDF: Unifying Neural Representations for High-Fidelity 3D Reconstruction of Complex Scenes with Reflections [92.38975002642455]
  We propose UniSDF, a general purpose 3D reconstruction method that can reconstruct large complex scenes with reflections. Our method is able to robustly reconstruct complex large-scale scenes with fine details and reflective surfaces.
  arXiv  Detail & Related papers  (2023-12-20T18:59:42Z)
• 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)
• Single-view 3D Scene Reconstruction with High-fidelity Shape and Texture [47.44029968307207]
  We propose a novel framework for simultaneous high-fidelity recovery of object shapes and textures from single-view images. Our approach utilizes the proposed Single-view neural implicit Shape and Radiance field (SSR) representations to leverage both explicit 3D shape supervision and volume rendering. A distinctive feature of our framework is its ability to generate fine-grained textured meshes while seamlessly integrating rendering capabilities into the single-view 3D reconstruction model.
  arXiv  Detail & Related papers  (2023-11-01T11:46:15Z)
• Neural Fields meet Explicit Geometric Representation for Inverse Rendering of Urban Scenes [62.769186261245416]
  We present a novel inverse rendering framework for large urban scenes capable of jointly reconstructing the scene geometry, spatially-varying materials, and HDR lighting from a set of posed RGB images with optional depth. Specifically, we use a neural field to account for the primary rays, and use an explicit mesh (reconstructed from the underlying neural field) for modeling secondary rays that produce higher-order lighting effects such as cast shadows.
  arXiv  Detail & Related papers  (2023-04-06T17:51:54Z)
• 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)
• Shape and Reflectance Reconstruction in Uncontrolled Environments by Differentiable Rendering [27.41344744849205]
  We propose an efficient method to reconstruct the scene's 3D geometry and reflectance from multi-view photography using conventional hand-held cameras. Our method also shows superior performance compared to state-of-the-art alternatives in novel view visually synthesis and quantitatively.
  arXiv  Detail & Related papers  (2021-10-25T14:09:10Z)
• Pix2Surf: Learning Parametric 3D Surface Models of Objects from Images [64.53227129573293]
  We investigate the problem of learning to generate 3D parametric surface representations for novel object instances, as seen from one or more views. We design neural networks capable of generating high-quality parametric 3D surfaces which are consistent between views. Our method is supervised and trained on a public dataset of shapes from common object categories.
  arXiv  Detail & Related papers  (2020-08-18T06:33:40Z)
• Vid2Curve: Simultaneous Camera Motion Estimation and Thin Structure Reconstruction from an RGB Video [90.93141123721713]
  Thin structures, such as wire-frame sculptures, fences, cables, power lines, and tree branches, are common in the real world. It is extremely challenging to acquire their 3D digital models using traditional image-based or depth-based reconstruction methods because thin structures often lack distinct point features and have severe self-occlusion. We propose the first approach that simultaneously estimates camera motion and reconstructs the geometry of complex 3D thin structures in high quality from a color video captured by a handheld camera.
  arXiv  Detail & Related papers  (2020-05-07T10:39:20Z)
• NodeSLAM: Neural Object Descriptors for Multi-View Shape Reconstruction [4.989480853499916]
  We present efficient and optimisable multi-class learned object descriptors together with a novel probabilistic and differential rendering engine. Our framework allows for accurate and robust 3D object reconstruction which enables multiple applications including robot grasping and placing, augmented reality, and the first object-level SLAM system.
  arXiv  Detail & Related papers  (2020-04-09T11:09:56Z)
• 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)
• Multiview Neural Surface Reconstruction by Disentangling Geometry and Appearance [46.488713939892136]
  We introduce a neural network that simultaneously learns the unknown geometry, camera parameters, and a neural architecture that approximates the light reflected from the surface towards the camera. We trained our network on real world 2D images of objects with different material properties, lighting conditions, and noisy camera materials from the DTU MVS dataset.
  arXiv  Detail & Related papers  (2020-03-22T10:20:13Z)

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.