GausSurf: Geometry-Guided 3D Gaussian Splatting for Surface Reconstruction

TL;DR

GausSurf introduces a geometry-guided 3D Gaussian Splatting method that enhances surface reconstruction quality by combining multi-view stereo guidance and normal priors, outperforming existing techniques in accuracy and efficiency.

Contribution

The paper proposes a novel geometry-guided approach for 3D Gaussian Splatting that integrates multi-view stereo and normal priors for improved surface reconstruction.

Findings

Outperforms state-of-the-art methods on DTU and Tanks and Temples datasets.

Achieves higher reconstruction quality with faster training times.

Effectively handles both texture-rich and texture-less scene regions.

Abstract

3D Gaussian Splatting has achieved impressive performance in novel view synthesis with real-time rendering capabilities. However, reconstructing high-quality surfaces with fine details using 3D Gaussians remains a challenging task. In this work, we introduce GausSurf, a novel approach to high-quality surface reconstruction by employing geometry guidance from multi-view consistency in texture-rich areas and normal priors in texture-less areas of a scene. We observe that a scene can be mainly divided into two primary regions: 1) texture-rich and 2) texture-less areas. To enforce multi-view consistency at texture-rich areas, we enhance the reconstruction quality by incorporating a traditional patch-match based Multi-View Stereo (MVS) approach to guide the geometry optimization in an iterative scheme. This scheme allows for mutual reinforcement between the optimization of Gaussians and patch-match refinement, which significantly improves the reconstruction results and accelerates the training process. Meanwhile, for the texture-less areas, we leverage normal priors from a pre-trained normal estimation model to guide optimization. Extensive experiments on the DTU and Tanks and Temples datasets demonstrate that our method surpasses state-of-the-art methods in terms of reconstruction quality and computation time.