@inproceedings{
chen2024vcrgaus,
title={{VCR}-GauS: View Consistent Depth-Normal Regularizer for Gaussian Surface Reconstruction},
author={Hanlin Chen and Fangyin Wei and Chen Li and Tianxin Huang and Yunsong Wang and Gim Hee Lee},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=axnjX20Ssl}
}
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 normal rendered from 3D Gaussians updates only the rotation parameter while neglecting 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. Our code will be made open-source upon paper acceptance.
VCR-GauS: View Consistent Depth-Normal Regularizer for Gaussian Surface Reconstruction
Hanlin Chen Fangyin Wei Chen Li Tianxin Huang Yunsong Wang Gim Hee Lee