图像三维高斯泼溅的图引导场景重建
Graph-Guided Scene Reconstruction from Images with 3D Gaussian Splatting
AI总结：
本文提出图引导的三维场景重建框架GraphGS，利用空间先验构建相机图，结合多视图一致性约束与自适应采样，优化三维高斯泼溅，实现高质量、高效的大规模场景重建，性能达到最先进水平。
摘要：
本文研究了一个开放性的研究挑战：从图像中重建高质量、大规模的三维开放场景。我们观察到现有方法存在各种局限性，例如需要精确的相机位姿作为输入以及密集的视点用于监督。为了进行有效且高效的三维场景重建，我们提出了一种新颖的图引导三维场景重建框架GraphGS。具体而言，给定由RGB相机在场景中捕获的一组图像，我们首先设计了一种基于空间先验的场景结构估计方法。该方法随后被用于创建包含相机拓扑信息的相机图。此外，我们提出将图引导的多视图一致性约束和自适应采样策略应用于三维高斯泼溅优化过程。这大大缓解了高斯点过拟合到特定稀疏视点的问题，并加速了三维重建过程。我们证明了GraphGS能够从图像实现高保真度的三维重建，并在多个数据集上的定量和定性评估中展现出最先进的性能。项目页面：https://3dagentworld.github.io/graphgs。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
