Deep Stereo: Learning to Predict New Views from the World's Imagery

  title={Deep Stereo: Learning to Predict New Views from the World's Imagery},
  author={John Flynn and Ivan Neulander and James Philbin and Noah Snavely},
  journal={2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
Deep networks have recently enjoyed enormous success when applied to recognition and classification problems in computer vision [22, 33], but their use in graphics problems has been limited ([23, 7] are notable recent exceptions). In this work, we present a novel deep architecture that performs new view synthesis directly from pixels, trained from a large number of posed image sets. In contrast to traditional approaches, which consist of multiple complex stages of processing, each of which… CONTINUE READING
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