VCR-GauS: View Consistent Depth-Normal Regularizer for Gaussian Surface Reconstruction

• URL: http://arxiv.org/abs/2406.05774v2
• Date: Wed, 30 Oct 2024 09:40:39 GMT
• Title: VCR-GauS: View Consistent Depth-Normal Regularizer for Gaussian Surface Reconstruction
• Authors: Hanlin Chen, Fangyin Wei, Chen Li, Tianxin Huang, Yunsong Wang, Gim Hee Lee,
• Abstract summary: We propose a Depth-Normal regularizer that directly couples normal with other geometric parameters, leading to full updates of the geometric parameters from normal regularization. We also introduce a densification and splitting strategy to regularize the size and distribution of 3D Gaussians for more accurate surface modeling.
• Score: 47.603017811399624
• License:
• Abstract: Although 3D Gaussian Splatting has been widely studied because of its realistic and efficient novel-view synthesis, it is still challenging to extract a high-quality surface from the point-based representation. Previous works improve the surface by incorporating geometric priors from the off-the-shelf normal estimator. However, there are two main limitations: 1) Supervising normals rendered from 3D Gaussians effectively updates the rotation parameter but is less effective for other geometric parameters; 2) The inconsistency of predicted normal maps across multiple views may lead to severe reconstruction artifacts. In this paper, we propose a Depth-Normal regularizer that directly couples normal with other geometric parameters, leading to full updates of the geometric parameters from normal regularization. We further propose a confidence term to mitigate inconsistencies of normal predictions across multiple views. Moreover, we also introduce a densification and splitting strategy to regularize the size and distribution of 3D Gaussians for more accurate surface modeling. Compared with Gaussian-based baselines, experiments show that our approach obtains better reconstruction quality and maintains competitive appearance quality at faster training speed and 100+ FPS rendering.

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