FatesGS: Fast and Accurate Sparse-View Surface Reconstruction using Gaussian Splatting with Depth-Feature Consistency

TL;DR

FatesGS introduces a fast, accurate sparse-view surface reconstruction method using Gaussian Splatting enhanced by depth-feature consistency, overcoming limitations of existing approaches that require dense views.

Contribution

The paper proposes a novel sparse-view reconstruction framework that leverages intra-view depth and multi-view feature consistency, enabling high-quality surface reconstruction without dense input views.

Findings

Outperforms state-of-the-art methods in accuracy and speed

Achieves 60x to 200x faster reconstruction on benchmark datasets

Produces fine-grained meshes without pre-training

Abstract

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.