Graph-Guided Scene Reconstruction from Images with 3D Gaussian Splatting
This addresses the challenge of 3D scene reconstruction from images for applications like virtual reality or robotics, but appears incremental as it builds on existing 3D Gaussian Splatting methods.
The paper tackles the problem of reconstructing high-quality, large 3D open scenes from images, achieving state-of-the-art performance by alleviating overfitting to sparse viewpoints and expediting the reconstruction process.
This paper investigates an open research challenge of reconstructing high-quality, large 3D open scenes from images. It is observed existing methods have various limitations, such as requiring precise camera poses for input and dense viewpoints for supervision. To perform effective and efficient 3D scene reconstruction, we propose a novel graph-guided 3D scene reconstruction framework, GraphGS. Specifically, given a set of images captured by RGB cameras on a scene, we first design a spatial prior-based scene structure estimation method. This is then used to create a camera graph that includes information about the camera topology. Further, we propose to apply the graph-guided multi-view consistency constraint and adaptive sampling strategy to the 3D Gaussian Splatting optimization process. This greatly alleviates the issue of Gaussian points overfitting to specific sparse viewpoints and expedites the 3D reconstruction process. We demonstrate GraphGS achieves high-fidelity 3D reconstruction from images, which presents state-of-the-art performance through quantitative and qualitative evaluation across multiple datasets. Project Page: https://3dagentworld.github.io/graphgs.