DGSfM: Depth-Guided Scale-Aware Global Structure-from-Motion

2026-07-10

AI Summary

Global Structure-from-Motion (SfM) is prone to errors caused by noisy baselines, weak view-graph constraints, and visually ambiguous matches due to its reliance on scale-ambiguous epipolar geometry. This work introduces monocular depth maps into the global SfM pipeline for the first time, proposing a depth-aware reconstruction framework. By leveraging depth priors, epipolar constraints are transformed into scale-aware relative pose constraints. The approach further enhances robustness and accuracy through view-graph filtering, depth-consistent match pruning, and depth-guided initialization of camera poses and 3D points. Evaluated on the ETH3D and IMC2021 benchmarks, the method consistently outperforms existing global SfM techniques under both sparse and dense feature matching frontends.

Abstract

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.

Problem

• Structure-from-Motion
• global SfM
• scale ambiguity
• epipolar geometry
• 3D reconstruction

Innovation

• Depth-Guided SfM
• Scale-Aware Pose Estimation
• Global Structure-from-Motion
• Monocular Depth Prior
• Depth-Consistency Filtering