用单目深度图提升全局重建精度，解决模糊尺度和错误匹配问题。
DGSfM: Depth-Guided Scale-Aware Global Structure-from-Motion
- 引入深度图作为先验，将模糊的对极几何转为有尺度信息的相对位姿约束。
- 在ETH3D和IMC2021上显著提升位姿精度，优于现有强基线方法。
- 适合需要高精度重建的稀疏与稠密匹配场景，尤其对弱视图连接有效。
全局结构光恢复（SfM）能从无序图像中高效恢复相机位姿与稀疏三维结构，但其依赖于尺度模糊的对极几何，使全局定位对噪声基线估计和弱视图图约束敏感，且视觉模糊图像对产生的假边缘会进一步恶化重建质量。本文提出DGSfM，一种基于深度图的全局SfM流程，利用单目深度图作为可扩展先验，同时保留显式的多视图优化。针对每对图像，使用深度感知的相对位姿求解器，将尺度模糊的对极约束转换为尺度感知的相对位姿约束。通过视图图过滤与基于深度一致性的对应点剪枝，抑制仅在对极几何下看似合理的错误匹配。最后，全局尺度平均与深度引导的位姿-点初始化，将单目深度图对齐至统一重建尺度，并为全局定位与捆绑调整提供稳定初值。在ETH3D和IMC2021上的实验表明，DGSfM在稀疏与稠密匹配前端均持续优于强基线方法，显著提升位姿精度。代码已开源。
原文摘要 · Abstract (English)
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.
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