G-MASt3R-SfM：基于图的视图剪枝与多阶段优化实现鲁棒SfM
G-MASt3R-SfM: Graph-based View Pruning and Multi-stage Optimization for Robust SfM
AI总结：
提出G-MASt3R-SfM管道，通过基于图的视图剪枝（GVP）和多阶段优化（MSO）模块，抑制非重叠图像对导致的错误匹配，在ETH3D数据集上实现相机位姿和三维重建的最先进精度。
摘要：
运动恢复结构（SfM）对于多视图三维重建至关重要，但其精度高度依赖于图像匹配的准确性。尽管最近的对应匹配方法MASt3R即使在具有挑战性的条件下也能实现鲁棒匹配，但它倾向于为非重叠图像对生成不正确的对应关系。因此，使用MASt3R的现有SfM方法（如MASt3R-SfM）由于直接将这些不可靠匹配纳入优化，导致位姿估计精度显著下降。为解决此问题，我们提出G-MASt3R-SfM，一种新颖的SfM管道，通过两个关键模块增强鲁棒性。首先，基于图的视图剪枝（GVP）模块根据匹配置信度构建场景图，并几何地剪除外点视图。其次，多阶段优化（MSO）模块通过将优化范围从局部一致性扩展到全局一致性，逐步细化相机参数。在ETH3D数据集上的实验表明，我们的方法在相机位姿估计和三维重建中均实现了最先进的精度，有效抑制了由外点引起的噪声。
英文摘要：
Structure from Motion (SfM) is essential for multi-view 3D reconstruction, however, its accuracy heavily relies on the accuracy of image matching. While the recent correspondence matching method, MASt3R, enables robust matching even under challenging conditions, it tends to generate incorrect correspondences for non-overlapping image pairs. Consequently, existing SfM methods using MASt3R, such as MASt3R-SfM, suffer from significant degradation in pose estimation accuracy as they incorporate these unreliable matches directly into optimization. To address this issue, we propose G-MASt3R-SfM, a novel SfM pipeline that enhances robustness through two key modules. First, the Graph-based View Pruning (GVP) module constructs a scene graph from matching confidence and geometrically prunes outlier views. Second, the Multi-Stage Optimization (MSO) module progressively refines camera parameters by expanding the optimization scope from local consistency to the global consistency. Experiments on the ETH3D dataset demonstrate that our method achieves state-of-the-art accuracy in both camera pose estimation and 3D reconstruction, effectively suppressing noise caused by outliers.
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。
