  Point cloud registration methods can effectively handle large-scale,
partially overlapping point cloud pairs. Despite its practicality, matching the
unbalanced pairs in terms of spatial extent and density has been overlooked and
rarely studied. We present a novel method, dubbed UPPNet, for Unbalanced Point
cloud Pair registration. We propose to incorporate a hierarchical framework
that effectively finds inlier correspondences by gradually reducing search
space. The proposed method first predicts subregions within target point cloud
that are likely to be overlapped with query. Then following super-point
matching and fine-grained refinement modules predict accurate inlier
correspondences between the target and query. Additional geometric constraints
are applied to refine the correspondences that satisfy spatial compatibility.
The proposed network can be trained in an end-to-end manner, predicting the
accurate rigid transformation with a single forward pass. To validate the
efficacy of the proposed method, we create a carefully designed benchmark,
named KITTI-UPP dataset, by augmenting the KITTI odometry dataset. Extensive
experiments reveal that the proposed method not only outperforms
state-of-the-art point cloud registration methods by large margins on KITTI-UPP
benchmark, but also achieves competitive results on the standard pairwise
registration benchmark including 3DMatch, 3DLoMatch, ScanNet, and KITTI, thus
showing the applicability of our method on various datasets. The source code
and dataset will be publicly released.
