Learning-based view synthesis for light field cameras

With the introduction of consumer light field cameras, light field imaging has recently become widespread. However, there is an inherent trade-off between the angular and spatial resolution, and thus, these cameras often sparsely sample in either spatial or angular domain. In this paper, we use machine learning to mitigate this trade-off. Specifically, we propose a novel learning-based approach to synthesize new views from a sparse set of input views. We build upon existing view synthesis techniques and break down the process into disparity and color estimation components. We use two sequential convolutional neural networks to model these two components and train both networks simultaneously by minimizing the…

• NS
  National Science Foundation

  Awards: 1617234, 1451830 and 1617234, 1451830
• N
  Nokia
• G
  Google
• UO
  University of California, San Diego
• OO
  Office of Naval Research

  Award: N00014152013