SPCNet: Stepwise Point Cloud Completion Network

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dc.contributor.authorHu, Feien_US
dc.contributor.authorChen, Honghuaen_US
dc.contributor.authorLu, Xuequanen_US
dc.contributor.authorZhu, Zheen_US
dc.contributor.authorWang, Junen_US
dc.contributor.authorWang, Weimingen_US
dc.contributor.authorWang, Fu Leeen_US
dc.contributor.authorWei, Mingqiangen_US
dc.contributor.editorUmetani, Nobuyukien_US
dc.contributor.editorWojtan, Chrisen_US
dc.contributor.editorVouga, Etienneen_US
dc.date.accessioned2022-10-04T06:39:37Z
dc.date.available2022-10-04T06:39:37Z
dc.date.issued2022
dc.description.abstractHow will you repair a physical object with large missings? You may first recover its global yet coarse shape and stepwise increase its local details. We are motivated to imitate the above physical repair procedure to address the point cloud completion task.We propose a novel stepwise point cloud completion network (SPCNet) for various 3D models with large missings. SPCNet has a hierarchical bottom-to-up network architecture. It fulfills shape completion in an iterative manner, which 1) first infers the global feature of the coarse result; 2) then infers the local feature with the aid of global feature; and 3) finally infers the detailed result with the help of local feature and coarse result. Beyond the wisdom of simulating the physical repair, we newly design a cycle loss to enhance the generalization and robustness of SPCNet. Extensive experiments clearly show the superiority of our SPCNet over the state-of-the-art methods on 3D point clouds with large missings. Code is available at https://github.com/1127368546/SPCNet.en_US
dc.description.number7
dc.description.sectionheadersPoint Cloud Generation
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume41
dc.identifier.doi10.1111/cgf.14665
dc.identifier.issn1467-8659
dc.identifier.pages153-164
dc.identifier.pages12 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.14665
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14665
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectCCS Concepts: Methods and Applications → Shape Recognition; Modeling → Point-based Graphics; Point-Based Modeling
dc.subjectMethods and Applications → Shape Recognition
dc.subjectModeling → Point
dc.subjectbased Graphics
dc.subjectPoint
dc.subjectBased Modeling
dc.titleSPCNet: Stepwise Point Cloud Completion Networken_US
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