One-Nearest Neighborhood Guides Inlier Estimation for Unsupervised Point
  Cloud Registration
Yongzhe Yuan Yue Wu Maoguo Gong Qiguang Miao A. K. Qin
The precision of unsupervised point cloud registration methods is typically
limited by the lack of reliable inlier estimation and self-supervised signal,
especially in partially overlapping scenarios. In this paper, we propose an
effective inlier estimation method for unsupervised point cloud registration by
capturing geometric structure consistency between the source point cloud and
its corresponding reference point cloud copy. Specifically, to obtain a high
quality reference point cloud copy, an One-Nearest Neighborhood (1-NN) point
cloud is generated by input point cloud. This facilitates matching map
construction and allows for integrating dual neighborhood matching scores of
1-NN point cloud and input point cloud to improve matching confidence.
Benefiting from the high quality reference copy, we argue that the neighborhood
graph formed by inlier and its neighborhood should have consistency between
source point cloud and its corresponding reference copy. Based on this
observation, we construct transformation-invariant geometric structure
representations and capture geometric structure consistency to score the inlier
confidence for estimated correspondences between source point cloud and its
reference copy. This strategy can simultaneously provide the reliable
self-supervised signal for model optimization. Finally, we further calculate
transformation estimation by the weighted SVD algorithm with the estimated
correspondences and corresponding inlier confidence. We train the proposed
model in an unsupervised manner, and extensive experiments on synthetic and
real-world datasets illustrate the effectiveness of the proposed method.