In this paper, we present a new 3D Gaussian Surface based method, GausSurf, for efficient and high-quality multiview surface reconstruction with geometric gauidance (1) from patch-match and normal priors. For texture-rich areas, we utilize multi-view consistency constraints to guide the optimization process. For texture-less regions, we incorporate normal priors from a pretrained model to provide supplementary supervision signals. By effectively integrating these geometric priors, our method achieves both high-quality and efficient surface reconstruction.
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Cite as below if you find this repository is helpful to your project:
@article{wang2024GausSurf,
title={GausSurf: Geometry-Guided 3D Gaussian Splatting for Surface Reconstruction},
author={Wang, Jiepeng and Liu, Yuan and Wang, Peng and Lin, Cheng and Hou, Junhui and Li, Xin and Komura, Taku and Wang, Wenping},
journal={arXiv preprint arXiv:XXXX},
year={2024}
}