Geometry-aware Feature Matching for Large-Scale Structure from Motion

• URL: http://arxiv.org/abs/2409.02310v3
• Date: Wed, 25 Sep 2024 19:15:04 GMT
• Title: Geometry-aware Feature Matching for Large-Scale Structure from Motion
• Authors: Gonglin Chen, Jinsen Wu, Haiwei Chen, Wenbin Teng, Zhiyuan Gao, Andrew Feng, Rongjun Qin, Yajie Zhao,
• Abstract summary: We introduce geometry cues in addition to color cues to fill gaps when there is less overlap in large-scale scenarios. Our approach ensures that the denser correspondences from detector-free methods are geometrically consistent and more accurate. It outperforms state-of-the-art feature matching methods on benchmark datasets.
• Score: 10.645087195983201
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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