  Disparity/depth estimation from sequences of stereo images is an important
element in 3D vision. Owing to occlusions, imperfect settings and homogeneous
luminance, accurate estimate of depth remains a challenging problem. Targetting
view synthesis, we propose a novel learning-based framework making use of
dilated convolution, densely connected convolutional modules, compact decoder
and skip connections. The network is shallow but dense, so it is fast and
accurate. Two additional contributions -- a non-linear adjustment of the depth
resolution and the introduction of a projection loss, lead to reduction of
estimation error by up to 20% and 25% respectively. The results show that our
network outperforms state-of-the-art methods with an average improvement in
accuracy of depth estimation and view synthesis by approximately 45% and 34%
respectively. Where our method generates comparable quality of estimated depth,
it performs 10 times faster than those methods.
