Recently, Gaussian Splatting has sparked a new trend in the field of computer vision. Apart from novel view synthesis, it has also been extended to the area of multi-view reconstruction. The latest methods facilitate complete, detailed surface reconstruction while ensuring fast training speed. However, these methods still require dense input views, and their output quality significantly degrades with sparse views. We observed that the Gaussian primitives tend to overfit the few training views, leading to noisy floaters and incomplete reconstruction surfaces. In this paper, we present an innovative sparse-view reconstruction framework that leverages intra-view depth and multi-view feature consistency to achieve remarkably accurate surface reconstruction. Specifically, we utilize monocular depth ranking information to supervise the consistency of depth distribution within patches and employ a smoothness loss to enhance the continuity of the distribution. To achieve finer surface reconstruction, we optimize the absolute position of depth through multi-view projection features. Extensive experiments on DTU and BlendedMVS demonstrate that our method outperforms state-of-the-art methods with a speedup of 60x to 200x, achieving swift and fine-grained mesh reconstruction without the need for costly pre-training.

本文研究了解决当前在稀疏视图下多视图重建质量下降的问题，提出了一种创新的稀疏视图重建框架，通过利用单目深度排名信息和多视图特征一致性，大幅提升了表面重建的准确性。实验表明，该方法在DTU和BlendedMVS数据集上实现了60倍至200倍的速度提升，同时无需昂贵的预训练，显著超越了当前最先进的方法。

FatesGS：基于深度特征一致性的快速准确稀疏视图表面重建