发表机构
Czech Technical University in Prague; Comenius University in Bratislava; Southeast University（布拉格捷克技术大学; 布拉迪斯拉发夸美纽斯大学; 东南大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对全局运动结构恢复中存在的问题，提出DGSfM方法，利用单目深度图，通过深度感知相对姿态求解器等技术，经视图图过滤等提高鲁棒性，实现稳定初始化，实验证明该方法在姿态精度上优于基线。
AI 中文摘要
全局运动结构恢复（SfM）是从无序图像中恢复相机姿态和稀疏3D结构的有效范例。但它对尺度模糊的极线几何的依赖，使全局定位对噪声基线估计和弱视图图约束敏感，视觉模糊对的虚假边缘会进一步降低重建效果。我们提出DGSfM，一种深度感知全局SfM管道，使用单目深度图作为可扩展先验，同时保留显式多视图优化。对每个图像对，用深度感知相对姿态求解器将尺度模糊的极线约束转换为尺度感知相对姿态约束。通过视图图过滤和基于深度一致性的对应关系修剪提高鲁棒性，抑制仅在极线几何下看似合理的虚假边缘和匹配。最后，全局尺度平均和深度引导的姿态点初始化将单目深度图对齐到共同重建尺度，为全局定位和束调整提供稳定初始化。在ETH3D和IMC2021上的实验表明，DGSfM在稀疏和密集匹配前端均持续优于强大的全局SfM基线，在姿态精度上有显著提升。
英文摘要
Global Structure-from-Motion (SfM) is an efficient paradigm for recovering camera poses and sparse 3D structure from unordered images. However, its reliance on scale-ambiguous epipolar geometry makes global positioning sensitive to noisy baseline estimates and weak view-graph constraints, while false edges from visually ambiguous pairs can further degrade reconstruction. We propose DGSfM, a depth-aware global SfM pipeline that uses monocular depth maps as a scalable prior while preserving explicit multi-view optimization. For each image pair, we use a depth-aware relative pose solver to convert scale-ambiguous epipolar constraints into scale-aware relative pose constraints. We further improve robustness through view-graph filtering and depth-consistency-based correspondence pruning, which suppress false edges and matches that remain plausible under epipolar geometry alone. Finally, global scale averaging and depth-guided pose-point initialization align monocular depth maps into a common reconstruction scale and provide stable initialization for global positioning and bundle adjustment. Experiments on ETH3D and IMC2021 show that DGSfM consistently improves over strong global SfM baselines across sparse and dense matching front-ends, achieving substantial gains in pose accuracy. Code is available at https://github.com/sithu31296/DGSfM.
