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Learning to Assemble Geometric Shapes

DC Field Value Language
dc.contributor.authorLee, Jinhwi-
dc.contributor.authorKim, Jungtaek-
dc.contributor.authorChung, Hyunsoo-
dc.contributor.authorPark, Jaesik-
dc.contributor.authorCho, Minsu-
dc.date.accessioned2024-05-09T04:12:26Z-
dc.date.available2024-05-09T04:12:26Z-
dc.date.created2024-05-08-
dc.date.issued2022-
dc.identifier.citationIJCAI International Joint Conference on Artificial Intelligence, pp.1046-1052-
dc.identifier.issn1045-0823-
dc.identifier.urihttps://hdl.handle.net/10371/201291-
dc.description.abstractAssembling parts into an object is a combinatorial problem that arises in a variety of contexts in the real world and involves numerous applications in science and engineering. Previous related work tackles limited cases with identical unit parts or jigsaw-style parts of textured shapes, which greatly mitigate combinatorial challenges of the problem. In this work, we introduce the more challenging problem of shape assembly, which involves textureless fragments of arbitrary shapes with indistinctive junctions, and then propose a learning-based approach to solving it. We demonstrate the effectiveness on shape assembly tasks with various scenarios, including the ones with abnormal fragments (e.g., missing and distorted), the different number of fragments, and different rotation discretization.-
dc.language영어-
dc.publisherInternational Joint Conferences on Artificial Intelligence-
dc.titleLearning to Assemble Geometric Shapes-
dc.typeArticle-
dc.citation.journaltitleIJCAI International Joint Conference on Artificial Intelligence-
dc.identifier.scopusid2-s2.0-85137872581-
dc.citation.endpage1052-
dc.citation.startpage1046-
dc.description.isOpenAccessN-
dc.contributor.affiliatedAuthorPark, Jaesik-
dc.type.docTypeConference Paper-
dc.description.journalClass1-
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  • College of Engineering
  • Dept. of Computer Science and Engineering
Research Area Computer Graphics, Computer Vision, Machine Learning, Robotics

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