VCR-GauS: View Consistent Depth-Normal Regularizer for Gaussian Surface Reconstruction
<|reference_start|>VCR-GauS: View Consistent Depth-Normal Regularizer for Gaussian Surface Reconstruction: 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.<|reference_end|>
@article{chen2024vcr-gaus:,
    title={VCR-GauS: View Consistent Depth-Normal Regularizer for Gaussian Surface
  Reconstruction},
    author={Hanlin Chen, Fangyin Wei, Chen Li, Tianxin Huang, Yunsong Wang, Gim
  Hee Lee},
    journal={arXiv preprint arXiv:2406.05774},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.05774},
    primaryClass={cs.CV}
}