Geometry-Aware Feature Matching for Large-Scale Structure from Motion
This work addresses a key bottleneck in 3D reconstruction for computer vision applications, offering incremental improvements in feature matching for challenging large-scale scenarios.
The paper tackles the problem of establishing dense correspondences across images in large-scale Structure from Motion, especially under significant view changes like air-to-ground with sparse overlap, by introducing a geometry-aware optimization method that improves correspondence density and accuracy, leading to better camera pose and point cloud results.
Establishing consistent and dense correspondences across multiple images is crucial for Structure from Motion (SfM) systems. Significant view changes, such as air-to-ground with very sparse view overlap, pose an even greater challenge to the correspondence solvers. We present a novel optimization-based approach that significantly enhances existing feature matching methods by introducing geometry cues in addition to color cues. This helps fill gaps when there is less overlap in large-scale scenarios. Our method formulates geometric verification as an optimization problem, guiding feature matching within detector-free methods and using sparse correspondences from detector-based methods as anchor points. By enforcing geometric constraints via the Sampson Distance, our approach ensures that the denser correspondences from detector-free methods are geometrically consistent and more accurate. This hybrid strategy significantly improves correspondence density and accuracy, mitigates multi-view inconsistencies, and leads to notable advancements in camera pose accuracy and point cloud density. It outperforms state-of-the-art feature matching methods on benchmark datasets and enables feature matching in challenging extreme large-scale settings.