Show simple item record

dc.contributor.authorGuerrero, Paulen_US
dc.contributor.authorKleiman, Yaniren_US
dc.contributor.authorOvsjanikov, Maksen_US
dc.contributor.authorMitra, Niloy J.en_US
dc.contributor.editorGutierrez, Diego and Sheffer, Allaen_US
dc.date.accessioned2018-04-14T18:23:04Z
dc.date.available2018-04-14T18:23:04Z
dc.date.issued2018
dc.identifier.issn1467-8659
dc.identifier.urihttp://dx.doi.org/10.1111/cgf.13343
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf13343
dc.description.abstractIn this paper, we propose PCPNET, a deep-learning based approach for estimating local 3D shape properties in point clouds. In contrast to the majority of prior techniques that concentrate on global or mid-level attributes, e.g., for shape classification or semantic labeling, we suggest a patch-based learning method, in which a series of local patches at multiple scales around each point is encoded in a structured manner. Our approach is especially well-adapted for estimating local shape properties such as normals (both unoriented and oriented) and curvature from raw point clouds in the presence of strong noise and multi-scale features. Our main contributions include both a novel multi-scale variant of the recently proposed PointNet architecture with emphasis on local shape information, and a series of novel applications in which we demonstrate how learning from training data arising from well-structured triangle meshes, and applying the trained model to noisy point clouds can produce superior results compared to specialized state-of-the-art techniques. Finally, we demonstrate the utility of our approach in the context of shape reconstruction, by showing how it can be used to extract normal orientation information from point clouds.en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectComputing methodologies
dc.subjectPoint
dc.subjectbased models
dc.subjectShape analysis
dc.subjectComputer systems organization
dc.subjectNeural networks
dc.titlePCPNet: Learning Local Shape Properties from Raw Point Cloudsen_US
dc.description.seriesinformationComputer Graphics Forum
dc.description.sectionheadersGeometry Learning
dc.description.volume37
dc.description.number2
dc.identifier.doi10.1111/cgf.13343
dc.identifier.pages75-85


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record