PCLC-Net: Point Cloud Completion in Arbitrary Poses using Learnable Canonical Space

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dc.contributor.authorXu, Hanmoen_US
dc.contributor.authorShuai, Qingyaoen_US
dc.contributor.authorChen, Xuejinen_US
dc.contributor.editorChen, Renjieen_US
dc.contributor.editorRitschel, Tobiasen_US
dc.contributor.editorWhiting, Emilyen_US
dc.date.accessioned2024-10-13T18:07:51Z
dc.date.available2024-10-13T18:07:51Z
dc.date.issued2024
dc.description.abstractRecovering the complete structure from partial point clouds in arbitrary poses is challenging. Recently, many efforts have been made to address this problem by developing SO(3)-equivariant completion networks or aligning the partial point clouds with a predefined canonical space before completion. However, these approaches are limited to random rotations only or demand costly pose annotation for model training. In this paper, we present a novel Network for Point cloud Completion with Learnable Canonical space (PCLC-Net) to reduce the need for pose annotations and extract SE(3)-invariant geometry features to improve the completion quality in arbitrary poses. Without pose annotations, our PCLC-Net utilizes self-supervised pose estimation to align the input partial point clouds to a canonical space that is learnable for an object category and subsequently performs shape completion in the learned canonical space. Our PCLC-Net can complete partial point clouds with arbitrary SE(3) poses without requiring pose annotations for supervision. Our PCLC-Net achieves state-of-the-art results on shape completion with arbitrary SE(3) poses on both synthetic and real scanned data. To the best of our knowledge, our method is the first to achieve shape completion in arbitrary poses without pose annotations during network training.en_US
dc.description.number7
dc.description.sectionheadersPoint Cloud Processing and Analysis II
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume43
dc.identifier.doi10.1111/cgf.15217
dc.identifier.issn1467-8659
dc.identifier.pages12 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.15217
dc.identifier.urihttps://diglib.eg.org/handle/10.1111/cgf15217
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies → Point-based models
dc.subjectComputing methodologies → Point
dc.subjectbased models
dc.titlePCLC-Net: Point Cloud Completion in Arbitrary Poses using Learnable Canonical Spaceen_US
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