SAD-GS: Shape-Aligned Depth-Supervised Gaussian Splatting

Abstract

This paper proposes SAD-GS, a depth-supervised Gaussian Splatting (GS) method that provides accurate 3D geometry reconstruction by introducing a shape-aligned depth supervision strategy. Depth information is widely used in various GS applications, such as dynamic scene reconstruction, real-time simultaneous lo-calization and mapping, and few-shot reconstruction. However, existing depth-supervised methods for GS all focus on the center and neglect the shape of Gaussians during training. This oversight can result in inaccurate surface geometry in the reconstruction and can harm downstream tasks like novel view synthesis, mesh reconstruction, and robot path planning. To address this, this paper proposes a shape-aligned loss, which aims to produce a smooth and precise reconstruction by adding extra constraints to the Gaussian shape. The proposed method is evaluated qualitatively and quantitatively on two publicly available datasets. The evaluation demonstrates that the proposed method provides state-of-the-art novel view rendering quality and mesh accuracy compared to existing depth-supervised GS methods. A project page is available at https://umauto-bots.github.io/sad_gs.