Self-Supervised Feature Learning from Partial Point Clouds via Pose Disentanglement

Meng Shiun Tsai, Pei Ze Chiang, Yi Hsuan Tsai, Wei Chen Chiu

研究成果: Conference contribution同行評審

1 引文 斯高帕斯(Scopus)

摘要

Self-supervised learning on point clouds has gained a lot of attention recently, since it addresses the label-efficiency and domain-gap problems on point cloud tasks. In this paper, we propose a novel self-supervised framework to learn informative features from partial point clouds. We leverage partial point clouds scanned by LiDAR that contain both content and pose attributes, and we show that disentangling such two factors from partial point clouds enhances feature learning. To this end, our framework consists of three main parts: 1) a completion network to capture holistic semantics of point clouds; 2) a pose regression network to understand the viewing angle where partial data is scanned from; 3) a partial reconstruction network to encourage the model to learn content and pose features. To demonstrate the robustness of the learnt feature representations, we conduct several downstream tasks including classification, part segmentation, and registration, with comparisons against state-of-the-art methods. Our method not only outperforms existing self-supervised methods, but also shows a better generalizability across synthetic and real-world datasets.

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1031-1038
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, Japan
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Japan
城市Kyoto
期間23/10/2227/10/22

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