PCRP: Unsupervised Point Cloud Object Retrieval and Pose Estimation

• URL: http://arxiv.org/abs/2202.07843v1
• Date: Wed, 16 Feb 2022 03:37:43 GMT
• Title: PCRP: Unsupervised Point Cloud Object Retrieval and Pose Estimation
• Authors: Pranav Kadam, Qingyang Zhou, Shan Liu, C.-C. Jay Kuo
• Abstract summary: An unsupervised point cloud object retrieval and pose estimation method, called PCRP, is proposed in this work. Experiments on the ModelNet40 dataset demonstrate the superior performance of PCRP in comparison with traditional and learning based methods.
• Score: 50.3020332934185
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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