Progressive Seed Generation Auto-encoder for Unsupervised Point Cloud Learning

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With the development of 3D scanning technologies, 3D vision tasks have become a popular research area. Owing to the large amount of data acquired by sensors, unsupervised learning is essential for understanding and utilizing point clouds without an expensive annotation process. In this paper, we propose a novel framework and an effective auto-encoder architecture named “PSG-Net” for reconstruction-based learning of point clouds. Unlike existing studies that used fixed or random 2D points, our framework generates input-dependent point-wise features for the latent point set. PSG-Net uses the encoded input to produce point-wise features through the seed generation module and extracts richer features in multiple stages with gradually increasing resolution by applying the seed feature propagation module progressively. We prove the effectiveness of PSG-Net experimentally; PSG-Net shows state-of-the-art performances in point cloud reconstruction and unsupervised classification, and achieves comparable performance to counterpart methods in supervised completion.
Publisher
Institute of Electrical and Electronics Engineers Inc.
Issue Date
2021-10
Language
English
Citation

18th IEEE/CVF International Conference on Computer Vision, ICCV 2021, pp.6393 - 6402

ISSN
1550-5499
DOI
10.1109/ICCV48922.2021.00635
URI
http://hdl.handle.net/10203/300179
Appears in Collection
EE-Conference Papers(학술회의논문)
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