SolidGS：用于稀疏视角表面重建的统一高斯泼溅
SolidGS: Consolidating Gaussian Surfel Splatting for Sparse-View Surface Reconstruction
AI总结：
本文提出SolidGS方法，通过采用更坚固的核函数统一高斯函数，结合几何正则化和单目法线估计，有效解决了稀疏视角下高斯泼溅表面重建的几何不一致问题，在多个数据集上优于现有方法。
摘要：
高斯泼溅在多视角图像的新视角合成和表面重建方面取得了显著进展。然而，现有方法仍难以仅用稀疏视角输入图像通过高斯泼溅重建高质量表面。本文提出一种名为SolidGS的新方法来解决此问题。我们观察到，由于高斯函数在几何渲染中的特性，重建的几何形状在多视角间可能存在严重的不一致性。这促使我们通过采用更坚固的核函数来统一所有高斯，从而有效提升表面重建质量。在几何正则化和单目法线估计的辅助下，我们的方法在广泛使用的DTU、Tanks-and-Temples和LLFF数据集上，其稀疏视角表面重建性能优于所有高斯泼溅方法和神经场方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
