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dc.contributor.authorChen, Lianggangxuen_US
dc.contributor.authorLu, Jialeen_US
dc.contributor.authorCai, Yiqingen_US
dc.contributor.authorWang, Changboen_US
dc.contributor.authorHe, Gaoqien_US
dc.contributor.editorUmetani, Nobuyukien_US
dc.contributor.editorWojtan, Chrisen_US
dc.contributor.editorVouga, Etienneen_US
dc.date.accessioned2022-10-04T06:39:31Z
dc.date.available2022-10-04T06:39:31Z
dc.date.issued2022
dc.identifier.issn1467-8659
dc.identifier.urihttps://doi.org/10.1111/cgf.14658
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14658
dc.description.abstract3D scene graph generation (SGG) aims to predict the class of objects and predicates simultaneously in one 3D point cloud scene with instance segmentation. Since the underlying semantic of 3D point clouds is spatial information, recent ideas of the 3D SGG task usually face difficulties in understanding global contextual semantic relationships and neglect the intrinsic 3D visual structures. To build the global scope of semantic relationships, we first propose two types of Semantic Clue (SC) from entity level and path level, respectively. SC can be extracted from the training set and modeled as the co-occurrence probability between entities. Then a novel Semantic Clue aware Graph Convolution Network (SC-GCN) is designed to explicitly model each SC of which the message is passed in their specific neighbor pattern. For constructing the interactions between the 3D visual and semantic modalities, a visual-language transformer (VLT) module is proposed to jointly learn the correlation between 3D visual features and class label embeddings. Systematic experiments on the 3D semantic scene graph (3DSSG) dataset show that our full method achieves state-of-the-art performance.en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectCCS Concepts: Computing methodologies → 3D point cloud understanding; Graph convolution network
dc.subjectComputing methodologies → 3D point cloud understanding
dc.subjectGraph convolution network
dc.titleExploring Contextual Relationships in 3D Cloud Points by Semantic Knowledge Miningen_US
dc.description.seriesinformationComputer Graphics Forum
dc.description.sectionheadersPoint Cloud Processing and Dataset Generation
dc.description.volume41
dc.description.number7
dc.identifier.doi10.1111/cgf.14658
dc.identifier.pages75-86
dc.identifier.pages12 pages


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  • 41-Issue 7
    Pacific Graphics 2022 - Symposium Proceedings

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