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    MVTN: Multi-View Transformation Network for 3D Shape Recognition

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    Type
    Preprint
    Authors
    Hamdi, Abdullah cc
    Giancola, Silvio
    Li, Bing
    Thabet, Ali Kassem cc
    Ghanem, Bernard cc
    KAUST Department
    Electrical Engineering Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Visual Computing Center (VCC)
    Date
    2020-11-26
    Permanent link to this record
    http://hdl.handle.net/10754/666178
    
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    Abstract
    Multi-view projection methods have shown the capability to reach state-of-the-art performance on 3D shape recognition. Most advances in multi-view representation focus on pooling techniques that learn to aggregate information from the different views, which tend to be heuristically set and fixed for all shapes. To circumvent the lack of dynamism of current multi-view methods, we propose to learn those viewpoints. In particular, we introduce a Multi-View Transformation Network (MVTN) that regresses optimal viewpoints for 3D shape recognition. By leveraging advances in differentiable rendering, our MVTN is trained end-to-end with any multi-view network and optimized for 3D shape classification. We show that MVTN can be seamlessly integrated into various multi-view approaches to exhibit clear performance gains in the tasks of 3D shape classification and shape retrieval without any extra training supervision. Furthermore, our MVTN improves multi-view networks to achieve state-of-the-art performance in rotation robustness and in object shape retrieval on ModelNet40.
    Publisher
    arXiv
    arXiv
    2011.13244
    Additional Links
    https://arxiv.org/pdf/2011.13244
    Collections
    Preprints; Electrical Engineering Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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