  In this article, we present a very lightweight neural network architecture,
trained on stereo data pairs, which performs view synthesis from one single
image. With the growing success of multi-view formats, this problem is indeed
increasingly relevant. The network returns a prediction built from disparity
estimation, which fills in wrongly predicted regions using a occlusion handling
technique. To do so, during training, the network learns to estimate the
left-right consistency structural constraint on the pair of stereo input
images, to be able to replicate it at test time from one single image. The
method is built upon the idea of blending two predictions: a prediction based
on disparity estimation, and a prediction based on direct minimization in
occluded regions. The network is also able to identify these occluded areas at
training and at test time by checking the pixelwise left-right consistency of
the produced disparity maps. At test time, the approach can thus generate a
left-side and a right-side view from one input image, as well as a depth map
and a pixelwise confidence measure in the prediction. The work outperforms
visually and metric-wise state-of-the-art approaches on the challenging KITTI
dataset, all while reducing by a very significant order of magnitude (5 or 10
times) the required number of parameters (6.5 M).
