DSGI-Net: Density-based Selective Grouping Point Cloud Learning Network for Indoor Scene

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dc.contributor.authorWen, Xinen_US
dc.contributor.authorDuan, Yaoen_US
dc.contributor.authorXu, Kaien_US
dc.contributor.authorZhu, Chenyangen_US
dc.contributor.editorChen, Renjieen_US
dc.contributor.editorRitschel, Tobiasen_US
dc.contributor.editorWhiting, Emilyen_US
dc.date.accessioned2024-10-13T18:07:54Z
dc.date.available2024-10-13T18:07:54Z
dc.date.issued2024
dc.description.abstractIndoor scene point clouds exhibit diverse distributions and varying levels of sparsity, characterized by more intricate geometry and occlusion compared to outdoor scenes or individual objects. Despite recent advancements in 3D point cloud analysis introducing various network architectures, there remains a lack of frameworks tailored to the unique attributes of indoor scenarios. To address this, we propose DSGI-Net, a novel indoor scene point cloud learning network that can be integrated into existing models. The key innovation of this work is selectively grouping more informative neighbor points in sparse regions and promoting semantic consistency of the local area where different instances are in proximity but belong to distinct categories. Furthermore, our method encodes both semantic and spatial relationships between points in local regions to reduce the loss of local geometric details. Extensive experiments on the ScanNetv2, SUN RGB-D, and S3DIS indoor scene benchmarks demonstrate that our method is straightforward yet effective.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.15218
dc.identifier.issn1467-8659
dc.identifier.pages12 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.15218
dc.identifier.urihttps://diglib.eg.org/handle/10.1111/cgf15218
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectCCS Concepts: Computing methodologies → Scene understanding
dc.subjectComputing methodologies → Scene understanding
dc.titleDSGI-Net: Density-based Selective Grouping Point Cloud Learning Network for Indoor Sceneen_US
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