用图像重建高质量大场景3D，无需精确相机位姿。
Graph-Guided Scene Reconstruction from Images with 3D Gaussian Splatting
- 构建相机拓扑图引导多视角一致性优化。
- 克服高斯点对稀疏视角过拟合问题，加速重建。
- 适合无精确位姿的开放场景3D重建任务。
本文研究从图像中重建高质量、大规模3D开放场景的开放性挑战。现有方法存在依赖精确相机位姿输入和密集视点监督等局限。为此，提出新型图引导3D场景重建框架GraphGS。给定一组由RGB相机拍摄的图像，首先设计基于空间先验的场景结构估计方法，生成包含相机拓扑信息的相机图。进一步将图引导的多视角一致性约束与自适应采样策略引入3D高斯泼溅优化过程，显著缓解高斯点对特定稀疏视角的过拟合问题，并加快3D重建速度。在多个数据集上通过定量与定性评估，GraphGS均实现高保真3D重建，达到当前最优性能。
原文摘要 · Abstract (English)
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.
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