RPG: Learning Recursive Point Cloud Generation

Wei Jan Kol, Chen Yi Chiu, Yu Liang Kuo, Wei Chen Chiu

研究成果: Conference contribution同行評審

2 引文 斯高帕斯(Scopus)

摘要

In this paper we propose a novel point cloud generator that is able to reconstruct and generate 3D point clouds composed of semantic parts. Given a latent representation of the target 3D model, the generation starts from a single point and gets expanded recursively to produce the high-resolution point cloud via a sequence of point expansion stages. During the recursive procedure of generation, we not only obtain the coarse-to-fine point clouds for the target 3D model from every expansion stage, but also unsupervisedly discover the semantic segmentation of the target model according to the hierarchical/parent-child relation between the points across expansion stages. Moreover, the expansion modules and other elements used in our recursive generator are mostly sharing weights thus making the overall framework light and efficient. Extensive experiments are conducted to show that our point cloud generator has comparable or even superior performance on both generation and reconstruction tasks in comparison to various baselines, and provides the consistent co-segmentation among instances of the same object class.

原文English
主出版物標題IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022
發行者Institute of Electrical and Electronics Engineers Inc.
頁面544-551
頁數8
ISBN(電子)9781665479271
DOIs
出版狀態Published - 2022
事件2022 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022 - Kyoto, 日本
持續時間: 23 10月 202227 10月 2022

出版系列

名字IEEE International Conference on Intelligent Robots and Systems
2022-October
ISSN(列印)2153-0858
ISSN(電子)2153-0866

Conference

Conference2022 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022
國家/地區日本
城市Kyoto
期間23/10/2227/10/22

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