3D Object Reconstruction from a Single Depth View with Adversarial Learning

@article{Yang20173DOR,
  title={3D Object Reconstruction from a Single Depth View with Adversarial Learning},
  author={Bo Yang and Hongkai Wen and Sen Wang and Ronald Clark and Andrew Markham and Agathoniki Trigoni},
  journal={2017 IEEE International Conference on Computer Vision Workshops (ICCVW)},
  year={2017},
  pages={679-688}
}
In this paper, we propose a novel 3D-RecGAN approach, which reconstructs the complete 3D structure of a given object from a single arbitrary depth view using generative adversarial networks. Unlike the existing work which typically requires multiple views of the same object or class labels to recover the full 3D geometry, the proposed 3D-RecGAN only takes the voxel grid representation of a depth view of the object as input, and is able to generate the complete 3D occupancy grid by filling in… CONTINUE READING
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