  Convolutional neural networks (CNN) have shown state-of-the-art results for
low-level computer vision problems such as stereo and monocular disparity
estimations, but still, have much room to further improve their performance in
terms of accuracy, numbers of parameters, etc. Recent works have uncovered the
advantages of using an unsupervised scheme to train CNN's to estimate monocular
disparity, where only the relatively-easy-to-obtain stereo images are needed
for training. We propose a novel encoder-decoder architecture that outperforms
previous unsupervised monocular depth estimation networks by (i) taking into
account ambiguities, (ii) efficient fusion between encoder and decoder features
with rectangular convolutions and (iii) domain transformations between encoder
and decoder. Our architecture outperforms the Monodepth baseline in all
metrics, even with a considerable reduction of parameters. Furthermore, our
architecture is capable of estimating a full disparity map in a single forward
pass, whereas the baseline needs two passes. We perform extensive experiments
to verify the effectiveness of our method on the KITTI dataset.

Image and Video Processing;Machine Learning