  Detecting the occlusion from stereo images or video frames is important to
many computer vision applications. Previous efforts focus on bundling it with
the computation of disparity or optical flow, leading to a chicken-and-egg
problem. In this paper, we leverage convolutional neural network to liberate
the occlusion detection task from the interleaved, traditional calculation
framework. We propose a Symmetric Network (SymmNet) to directly exploit
information from an image pair, without estimating disparity or motion in
advance. The proposed network is structurally left-right symmetric to learn the
binocular occlusion simultaneously, aimed at jointly improving both results.
The comprehensive experiments show that our model achieves state-of-the-art
results on detecting the stereo and motion occlusion.
