Single-image depth inference using generative adversarial networks

HIGHLIGHTS

• who: Daniel Stanley Tan and collaborators from the Department of Computer Science and Information Engineering, National Taiwan University of Science and have published the Article: Single-Image Depth Inference Using Generative Adversarial Networks, in the Journal: Sensors 2019, 19, 1708 of /2019/
• what: The authors propose a novel generative adversarial network that has an encoder-decoder type generator with residual transposed convolution blocks trained with an adversarial loss. The aim is to represent the spatial structure of a scene. The authors propose a generative approach using an encoder-decoder type network to synthesize the depth . . .
  

   

  

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