Multi-view Convolutional Neural Networks for 3D Shape Recognition

@article{Su2015MultiviewCN,
  title={Multi-view Convolutional Neural Networks for 3D Shape Recognition},
  author={Hang Su and Subhransu Maji and Evangelos Kalogerakis and Erik G. Learned-Miller},
  journal={2015 IEEE International Conference on Computer Vision (ICCV)},
  year={2015},
  pages={945-953}
}
A longstanding question in computer vision concerns the representation of 3D shapes for recognition: should 3D shapes be represented with descriptors operating on their native 3D formats, such as voxel grid or polygon mesh, or can they be effectively represented with view-based descriptors? We address this question in the context of learning to recognize 3D shapes from a collection of their rendered views on 2D images. We first present a standard CNN architecture trained to recognize the shapes… CONTINUE READING
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