GURecon: Learning Detailed 3D Geometric Uncertainties for Neural Surface Reconstruction

• URL: http://arxiv.org/abs/2412.14939v2
• Date: Fri, 20 Dec 2024 10:02:01 GMT
• Title: GURecon: Learning Detailed 3D Geometric Uncertainties for Neural Surface Reconstruction
• Authors: Zesong Yang, Ru Zhang, Jiale Shi, Zixiang Ai, Boming Zhao, Hujun Bao, Luwei Yang, Zhaopeng Cui,
• Abstract summary: We present a novel framework, GURecon, which establishes a geometric uncertainty field for the neural surface based on geometric consistency. Experiments on various datasets demonstrate the superiority of GURecon in modeling 3D geometric uncertainty.
• Score: 46.32074326890457
• License:
• Abstract: Neural surface representation has demonstrated remarkable success in the areas of novel view synthesis and 3D reconstruction. However, assessing the geometric quality of 3D reconstructions in the absence of ground truth mesh remains a significant challenge, due to its rendering-based optimization process and entangled learning of appearance and geometry with photometric losses. In this paper, we present a novel framework, i.e, GURecon, which establishes a geometric uncertainty field for the neural surface based on geometric consistency. Different from existing methods that rely on rendering-based measurement, GURecon models a continuous 3D uncertainty field for the reconstructed surface, and is learned by an online distillation approach without introducing real geometric information for supervision. Moreover, in order to mitigate the interference of illumination on geometric consistency, a decoupled field is learned and exploited to finetune the uncertainty field. Experiments on various datasets demonstrate the superiority of GURecon in modeling 3D geometric uncertainty, as well as its plug-and-play extension to various neural surface representations and improvement on downstream tasks such as incremental reconstruction. The code and supplementary material are available on the project website: https://zju3dv.github.io/GURecon/.

Related papers

• AniSDF: Fused-Granularity Neural Surfaces with Anisotropic Encoding for High-Fidelity 3D Reconstruction [55.69271635843385]
  We present AniSDF, a novel approach that learns fused-granularity neural surfaces with physics-based encoding for high-fidelity 3D reconstruction. Our method boosts the quality of SDF-based methods by a great scale in both geometry reconstruction and novel-view synthesis.
  arXiv  Detail & Related papers  (2024-10-02T03:10:38Z)
• CVRecon: Rethinking 3D Geometric Feature Learning For Neural Reconstruction [12.53249207602695]
  We propose an end-to-end 3D neural reconstruction framework CVRecon. We exploit the rich geometric embedding in the cost volumes to facilitate 3D geometric feature learning.
  arXiv  Detail & Related papers  (2023-04-28T05:30:19Z)
• HR-NeuS: Recovering High-Frequency Surface Geometry via Neural Implicit Surfaces [6.382138631957651]
  We present High-Resolution NeuS, a novel neural implicit surface reconstruction method. HR-NeuS recovers high-frequency surface geometry while maintaining large-scale reconstruction accuracy. We demonstrate through experiments on DTU and BlendedMVS datasets that our approach produces 3D geometries that are qualitatively more detailed and quantitatively of similar accuracy compared to previous approaches.
  arXiv  Detail & Related papers  (2023-02-14T02:25:16Z)
• Recovering Fine Details for Neural Implicit Surface Reconstruction [3.9702081347126943]
  We present D-NeuS, a volume rendering neural implicit surface reconstruction method capable to recover fine geometry details. We impose multi-view feature consistency on the surface points, derived by interpolating SDF zero-crossings from sampled points along rays. Our method reconstructs high-accuracy surfaces with details, and outperforms the state of the art.
  arXiv  Detail & Related papers  (2022-11-21T10:06:09Z)
• Learning Neural Radiance Fields from Multi-View Geometry [1.1011268090482573]
  We present a framework, called MVG-NeRF, that combines Multi-View Geometry algorithms and Neural Radiance Fields (NeRF) for image-based 3D reconstruction. NeRF has revolutionized the field of implicit 3D representations, mainly due to a differentiable rendering formulation that enables high-quality and geometry-aware novel view synthesis.
  arXiv  Detail & Related papers  (2022-10-24T08:53:35Z)
• Uncertainty Guided Policy for Active Robotic 3D Reconstruction using Neural Radiance Fields [82.21033337949757]
  This paper introduces a ray-based volumetric uncertainty estimator, which computes the entropy of the weight distribution of the color samples along each ray of the object's implicit neural representation. We show that it is possible to infer the uncertainty of the underlying 3D geometry given a novel view with the proposed estimator. We present a next-best-view selection policy guided by the ray-based volumetric uncertainty in neural radiance fields-based representations.
  arXiv  Detail & Related papers  (2022-09-17T21:28:57Z)
• SNeS: Learning Probably Symmetric Neural Surfaces from Incomplete Data [77.53134858717728]
  We build on the strengths of recent advances in neural reconstruction and rendering such as Neural Radiance Fields (NeRF) We apply a soft symmetry constraint to the 3D geometry and material properties, having factored appearance into lighting, albedo colour and reflectivity. We show that it can reconstruct unobserved regions with high fidelity and render high-quality novel view images.
  arXiv  Detail & Related papers  (2022-06-13T17:37:50Z)
• Multi-view 3D Reconstruction of a Texture-less Smooth Surface of Unknown Generic Reflectance [86.05191217004415]
  Multi-view reconstruction of texture-less objects with unknown surface reflectance is a challenging task. This paper proposes a simple and robust solution to this problem based on a co-light scanner.
  arXiv  Detail & Related papers  (2021-05-25T01:28:54Z)
• 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)

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.