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.

翻译：在这项工作中,提议采用一个称为PCRP的未监督点云天检索和估计方法,并假定存在一个包含点云天的画廊点云,并配有定向信息。PCRP试图将未知点云天与画廊中的对象进行登记,以便实现基于内容的天体检索,并共同作出估计,而点云登记任务建立在未经监督的R-PointHop方法的强化版本之上。在模型Net40数据集上进行的实验表明,与传统和基于学习的方法相比,PCRP的性能优异。