MODNet: Multi-offset Point Cloud Denoising Network Customized for Multi-scale Patches

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dc.contributor.authorHuang, Anyien_US
dc.contributor.authorXie, Qianen_US
dc.contributor.authorWang, Zhoutaoen_US
dc.contributor.authorLu, Deningen_US
dc.contributor.authorWei, Mingqiangen_US
dc.contributor.authorWang, Junen_US
dc.contributor.editorUmetani, Nobuyukien_US
dc.contributor.editorWojtan, Chrisen_US
dc.contributor.editorVouga, Etienneen_US
dc.date.accessioned2022-10-04T06:39:34Z
dc.date.available2022-10-04T06:39:34Z
dc.date.issued2022
dc.description.abstractThe intricacy of 3D surfaces often results cutting-edge point cloud denoising (PCD) models in surface degradation including remnant noise, wrongly-removed geometric details. Although using multi-scale patches to encode the geometry of a point has become the common wisdom in PCD, we find that simple aggregation of extracted multi-scale features can not adaptively utilize the appropriate scale information according to the geometric information around noisy points. It leads to surface degradation, especially for points close to edges and points on complex curved surfaces. We raise an intriguing question - if employing multi-scale geometric perception information to guide the network to utilize multi-scale information, can eliminate the severe surface degradation problem? To answer it, we propose a Multi-offset Denoising Network (MODNet) customized for multi-scale patches. First, we extract the low-level feature of three scales patches by patch feature encoders. Second, a multi-scale perception module is designed to embed multi-scale geometric information for each scale feature and regress multi-scale weights to guide a multi-offset denoising displacement. Third, a multi-offset decoder regresses three scale offsets, which are guided by the multi-scale weights to predict the final displacement by weighting them adaptively. Experiments demonstrate that our method achieves new state-of-the-art performance on both synthetic and real-scanned datasets. Our code is publicly available at https://github.com/hay-001/MODNet.en_US
dc.description.number7
dc.description.sectionheadersPoint Cloud Processing and Dataset Generation
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume41
dc.identifier.doi10.1111/cgf.14661
dc.identifier.issn1467-8659
dc.identifier.pages109-119
dc.identifier.pages11 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.14661
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14661
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectCCS Concepts: Computing methodologies → Point-based models; Shape analysis
dc.subjectComputing methodologies → Point
dc.subjectbased models
dc.subjectShape analysis
dc.titleMODNet: Multi-offset Point Cloud Denoising Network Customized for Multi-scale Patchesen_US
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