Robust Feature Matching for 3D Point Clouds with Progressive Consistency Voting.

Feature matching for 3D point clouds is a fundamental yet challenging problem in remote sensing and 3D computer vision. However, due to a number of nuisances, the initial feature correspondences generated by matching local keypoint descriptors may contain many outliers (incorrect correspondences). To remove outliers, this paper presents a robust method called progressive consistency voting (PCV). PCV aims at assigning a reliable confidence score to each correspondence such that reasonable correspondences can be achieved by simply finding top-scored ones. To compute the confidence score, we suggest fully utilizing the geometric consistency cue between correspondences and propose a voting-based scheme. In addition, we progressively mine convincing voters from the initial correspondence set and optimize the scoring result by considering top-scored correspondences at the last iteration. Experiments on several standard datasets verify that PCV outperforms five state-of-the-art methods under almost all tested conditions and is robust to noise, data decimation, clutter, occlusion, and data modality change. We also apply PCV to point cloud registration and show that it can significantly improve the registration performance.