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dc.contributor.authorDu, Dongen_US
dc.contributor.authorZhu, Hemingen_US
dc.contributor.authorNie, Yinyuen_US
dc.contributor.authorHan, Xiaoguangen_US
dc.contributor.authorCui, Shuguangen_US
dc.contributor.authorYu, Yizhouen_US
dc.contributor.authorLiu, Ligangen_US
dc.contributor.editorBenes, Bedrich and Hauser, Helwigen_US
dc.date.accessioned2021-02-27T19:02:30Z
dc.date.available2021-02-27T19:02:30Z
dc.date.issued2021
dc.identifier.issn1467-8659
dc.identifier.urihttps://doi.org/10.1111/cgf.14184
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14184
dc.description.abstractModeling 3D objects on existing software usually requires a heavy amount of interactions, especially for users who lack basic knowledge of 3D geometry. Sketch‐based modeling is a solution to ease the modelling procedure and thus has been researched for decades. However, modelling a man‐made shape with complex structures remains challenging. Existing methods adopt advanced deep learning techniques to map holistic sketches to 3D shapes. They are still bottlenecked to deal with complicated topologies. In this paper, we decouple the task of sketch2shape into a part generation module and a part assembling module, where deep learning methods are leveraged for the implementation of both modules. By changing the focus from holistic shapes to individual parts, it eases the learning process of the shape generator and guarantees high‐quality outputs. With the learned automated part assembler, users only need a little manual tuning to obtain a desired layout. Extensive experiments and user studies demonstrate the usefulness of our proposed system.en_US
dc.publisher© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltden_US
dc.subjectmodelling interfaces
dc.subjectmodelling
dc.subjectpart assembly
dc.titleLearning Part Generation and Assembly for Sketching Man‐Made Objectsen_US
dc.description.seriesinformationComputer Graphics Forum
dc.description.sectionheadersArticles
dc.description.volume40
dc.description.number1
dc.identifier.doi10.1111/cgf.14184
dc.identifier.pages222-233


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