  Unsupervised deep learning methods have shown promising performance for
single-image depth estimation. Since most of these methods use binocular stereo
pairs for self-supervision, the depth range is generally limited.
Small-baseline stereo pairs provide small depth range but handle occlusions
well. On the other hand, stereo images acquired with a wide-baseline rig cause
occlusions-related errors in the near range but estimate depth well in the far
range. In this work, we propose to integrate the advantages of the small and
wide baselines. By training the network using three horizontally aligned views,
we obtain accurate depth predictions for both close and far ranges. Our
strategy allows to infer multi-baseline depth from a single image. This is
unlike previous multi-baseline systems which employ more than two cameras. The
qualitative and quantitative results show the superior performance of
multi-baseline approach over previous stereo-based monocular methods. For 0.1
to 80 meters depth range, our approach decreases the absolute relative error of
depth by 24% compared to Monodepth2. Our approach provides 21 frames per second
on a single Nvidia1080 GPU, making it useful for practical applications.
