  Deep convolutional neural networks (DCNN) have recently shown promising
results in low-level computer vision problems such as optical flow and
disparity estimation, but still, have much room to further improve their
performance. In this paper, we propose a novel sub-pixel convolution-based
encoder-decoder network for optical flow and disparity estimations, which can
extend FlowNetS and DispNet by replacing the deconvolution layers with
sup-pixel convolution blocks. By using sub-pixel refinement and estimation on
the decoder stages instead of deconvolution, we can significantly improve the
estimation accuracy for optical flow and disparity, even with reduced numbers
of parameters. We show a supervised end-to-end training of our proposed
networks for optical flow and disparity estimations, and an unsupervised
end-to-end training for monocular depth and pose estimations. In order to
verify the effectiveness of our proposed networks, we perform intensive
experiments for (i) optical flow and disparity estimations, and (ii) monocular
depth and pose estimations. Throughout the extensive experiments, our proposed
networks outperform the baselines such as FlowNetS and DispNet in terms of
estimation accuracy and training times.
