Dense Graph Convolutional Neural Networks on 3D Meshes for 3D Object Segmentation and Classification

  title={Dense Graph Convolutional Neural Networks on 3D Meshes for 3D Object Segmentation and Classification},
  author={Wenming Tang and Guoping Qiu},
  journal={Image Vis. Comput.},
1 Citations

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A deep learning model is proposed for the mesh semantic segmentation, and graph concept and transformer blocks are employed for mesh representation and feature learning to efficiently capture local geometry and neighbourhood context within the mesh.



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  • Xin WeiRuixuan YuJian Sun
  • Computer Science
    2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2020
A novel view-based Graph Convolutional Neural Network, dubbed as view-GCN, to recognize 3D shape based on graph representation of multiple views in flexible view configurations, which is a hierarchical network based on local and non-local graph convolution for feature transform, and selective view-sampling for graph coarsening.

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