Learning Barycentric Representations of 3D Shapes for Sketch-Based 3D Shape Retrieval

@article{Xie2017LearningBR,
  title={Learning Barycentric Representations of 3D Shapes for Sketch-Based 3D Shape Retrieval},
  author={Jin Xie and Guoxian Dai and Fan Zhu and Yi Fang},
  journal={2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2017},
  pages={3615-3623}
}
Retrieving 3D shapes with sketches is a challenging problem since 2D sketches and 3D shapes are from two heterogeneous domains, which results in large discrepancy between them. In this paper, we propose to learn barycenters of 2D projections of 3D shapes for sketch-based 3D shape retrieval. Specifically, we first use two deep convolutional neural networks (CNNs) to extract deep features of sketches and 2D projections of 3D shapes. For 3D shapes, we then compute the Wasserstein barycenters of… CONTINUE READING
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