  An unsupervised point cloud object retrieval and pose estimation method,
called PCRP, is proposed in this work. It is assumed that there exists a
gallery point cloud set that contains point cloud objects with given pose
orientation information. PCRP attempts to register the unknown point cloud
object with those in the gallery set so as to achieve content-based object
retrieval and pose estimation jointly, where the point cloud registration task
is built upon an enhanced version of the unsupervised R-PointHop method.
Experiments on the ModelNet40 dataset demonstrate the superior performance of
PCRP in comparison with traditional and learning based methods.
