Traditional point cloud registration methods require
large overlap between scans, which imposes strict constraints on
data acquisition. To facilitate registration, users have to carefully
position scanners to ensure sufficient overlap. In this work, we
propose to use high-level structural information (i.e., plane/line
features and their inter-relationship) for registration, which is
capable of registering point clouds with small overlap, allowing
more freedom in data acquisition. We design a novel plane/linebased descriptor dedicated to establishing structure level correspondences between point clouds. Based on this descriptor,
we propose a simple but effective registration algorithm. We
also provide a dataset of real-world scenes containing a larger
number of scans with a wide range of overlap. Experiments and
comparisons with state-of-the-art methods on various datasets
reveal that our method is superior to existing techniques. Though
the proposed algorithm outperforms state-of-the-art methods
on the most challenging dataset, the point cloud registration
problem is still far from being solved, leaving significant room
for improvement and future work.