WarpNet: Weakly Supervised Matching for Single-View Reconstruction

@article{Kanazawa2016WarpNetWS,
  title={WarpNet: Weakly Supervised Matching for Single-View Reconstruction},
  author={Angjoo Kanazawa and David W. Jacobs and Manmohan Krishna Chandraker},
  journal={2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2016},
  pages={3253-3261}
}
We present an approach to matching images of objects in fine-grained datasets without using part annotations, with an application to the challenging problem of weakly supervised single-view reconstruction. This is in contrast to prior works that require part annotations, since matching objects across class and pose variations is challenging with appearance features alone. We overcome this challenge through a novel deep learning architecture, WarpNet, that aligns an object in one image with a… CONTINUE READING
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