SolidGS: Consolidating Gaussian Surfel Splatting for Sparse-View Surface Reconstruction

• URL: http://arxiv.org/abs/2412.15400v1
• Date: Thu, 19 Dec 2024 21:04:43 GMT
• Title: SolidGS: Consolidating Gaussian Surfel Splatting for Sparse-View Surface Reconstruction
• Authors: Zhuowen Shen, Yuan Liu, Zhang Chen, Zhong Li, Jiepeng Wang, Yongqing Liang, Zhengming Yu, Jingdong Zhang, Yi Xu, Scott Schaefer, Xin Li, Wenping Wang,
• Abstract summary: We propose a novel method called SolidGS to address this problem.<n>We observed that the reconstructed geometry can be severely inconsistent across multi-views.<n>With the additional help of geometrical regularization and monocular normal estimation, our method achieves superior performance on the sparse view surface reconstruction.
• Score: 48.228533595941556
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Gaussian splatting has achieved impressive improvements for both novel-view synthesis and surface reconstruction from multi-view images. However, current methods still struggle to reconstruct high-quality surfaces from only sparse view input images using Gaussian splatting. In this paper, we propose a novel method called SolidGS to address this problem. We observed that the reconstructed geometry can be severely inconsistent across multi-views, due to the property of Gaussian function in geometry rendering. This motivates us to consolidate all Gaussians by adopting a more solid kernel function, which effectively improves the surface reconstruction quality. With the additional help of geometrical regularization and monocular normal estimation, our method achieves superior performance on the sparse view surface reconstruction than all the Gaussian splatting methods and neural field methods on the widely used DTU, Tanks-and-Temples, and LLFF datasets.

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