Fast Depth Map Super-Resolution Using Deep Neural Network
Depth map super-resolution is a challenging computer vision problem. In this paper, we present two deep convolutional neural networks solving the problem of single depth map super-resolution. Both networks learn residual decomposition and trained with specific perceptual loss improving sharpness and perceptive quality of the upsampled depth map. Several experiments on various depth super-resolution benchmark datasets show state-of-art performance in terms of RMSE, SSIM, and PSNR metrics while allowing us to process depth super-resolution in real time with over 25-30 frames per second rate.