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Detecting the occlusion from stereo images or video frames is important tomany computer vision applications. Previous efforts focus on bundling it withthe computation of disparity or optical flow, leading to a chicken-and-eggproblem. In this paper, we leverage convolutional neural network to liberatethe occlusion detection task from the interleaved, traditional calculationframework. We propose a Symmetric Network (SymmNet) to directly exploitinformation from an image pair, without estimating disparity or motion inadvance. The proposed network is structurally left-right symmetric to learn thebinocular occlusion simultaneously, aimed at jointly improving both results.The comprehensive experiments show that our model achieves state-of-the-artresults on detecting the stereo and motion occlusion.

SymmNet: A Symmetric Convolutional Neural Network for Occlusion Detection