TreeGCN-ED: A Tree-Structured Graph-Based Autoencoder Framework For Point Cloud Processing

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dc.contributor.authorSingh, Prajwalen_US
dc.contributor.authorTiwari, Ashishen_US
dc.contributor.authorSadekar, Kaustubhen_US
dc.contributor.authorRaman, Shanmuganathanen_US
dc.contributor.editorChaine, Raphaëlleen_US
dc.contributor.editorDeng, Zhigangen_US
dc.contributor.editorKim, Min H.en_US
dc.date.accessioned2023-10-09T07:42:53Z
dc.date.available2023-10-09T07:42:53Z
dc.date.issued2023
dc.description.abstractPoint cloud is a widely used technique for representing and storing 3D geometric data. Several methods have been proposed for processing point clouds for tasks such as 3D shape classification and clustering. This work presents a tree-structured autoencoder framework to generate robust embeddings of point clouds through hierarchical information aggregation using graph convolution. We visualize the t-SNE map to highlight the ability of learned embeddings to distinguish between different object classes. We further demonstrate the robustness of these embeddings in applications such as point cloud interpolation, completion, and single image-based point cloud reconstruction. The anonymized code is available here for research purposes.en_US
dc.description.sectionheadersPosters
dc.description.seriesinformationPacific Graphics Short Papers and Posters
dc.identifier.doi10.2312/pg.20231278
dc.identifier.isbn978-3-03868-234-9
dc.identifier.pages105-106
dc.identifier.pages2 pages
dc.identifier.urihttps://doi.org/10.2312/pg.20231278
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/pg20231278
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies -> Shape representations
dc.subjectComputing methodologies
dc.subjectShape representations
dc.titleTreeGCN-ED: A Tree-Structured Graph-Based Autoencoder Framework For Point Cloud Processingen_US
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