SilNet : Single- and Multi-View Reconstruction by Learning from Silhouettes

@article{Wiles2017SilNetS,
  title={SilNet : Single- and Multi-View Reconstruction by Learning from Silhouettes},
  author={Olivia Wiles and Andrew Zisserman},
  journal={CoRR},
  year={2017},
  volume={abs/1711.07888}
}
The objective of this paper is 3D shape understanding from single and multiple images. To this end, we introduce a new deep-learning architecture and loss function, SilNet, that can handle multiple views in an order-agnostic manner. The architecture is fully convolutional, and for training we use a proxy task of silhouette prediction, rather than directly learning a mapping from 2D images to 3D shape as has been the target in most recent work. We demonstrate that with the SilNet architecture… CONTINUE READING
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