PCRP: Unsupervised Point Cloud Object Retrieval and Pose Estimation
This addresses the problem of 3D object analysis without labeled data for researchers in computer vision, but it is incremental as it builds on an existing method.
The paper tackles unsupervised point cloud object retrieval and pose estimation by proposing PCRP, which registers unknown point clouds with a gallery set using an enhanced R-PointHop method, achieving superior performance on the ModelNet40 dataset compared to traditional and learning-based methods.
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.