改进SfM的匹配噪声问题，提升3D重建精度。
G-MASt3R-SfM: Graph-based View Pruning and Multi-stage Optimization for Robust SfM
- 用图结构筛选可靠视角，剔除错误匹配。
- 分阶段优化相机参数，从局部到全局提升一致性。
- 适合高难度场景下的3D重建任务。
结构光恢复（SfM）对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。尽管近期的MASt3R匹配方法在复杂条件下仍具鲁棒性，却容易为非重叠图像对生成错误对应点。因此，使用MASt3R的现有SfM方法（如MASt3R-SfM）在将这些不可靠匹配直接用于优化时，会显著降低姿态估计精度。为此，我们提出G-MASt3R-SfM，一种新SfM流程，通过两个关键模块增强鲁棒性：首先，基于图的视角裁剪（GVP）模块利用匹配置信度和几何信息构建场景图，并剔除异常视角；其次，多阶段优化（MSO）模块逐步扩展优化范围，从局部一致性推进至全局一致性以精炼相机参数。在ETH3D数据集上的实验表明，该方法在相机姿态估计与三维重建方面均达到当前最优水平，有效抑制了由异常值引起的噪声。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。