论文标题
PCRP：无监督点云对象检索和姿势估计
PCRP: Unsupervised Point Cloud Object Retrieval and Pose Estimation
论文作者
论文摘要
在这项工作中提出了一种无监督的点云对象检索和姿势估计方法，称为PCRP。假定存在一个画廊点云集，其中包含带有给定姿势取向信息的点云对象。 PCRP试图将未知点云对象与画廊集中的那些对象注册，以共同实现基于内容的对象检索和姿势估计，其中点云注册任务是建立在不受监督的R-Pointhop方法的增强版本上的。 ModelNet40数据集上的实验证明了与传统和基于学习的方法相比，PCRP的出色性能。
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.
