Self-Supervised Learning for Stereo Matching with Self-Improving Ability

4 Sep 2017 Yiran Zhong Yuchao Dai Hongdong Li

Exiting deep-learning based dense stereo matching methods often rely on ground-truth disparity maps as the training signals, which are however not always available in many situations. In this paper, we design a simple convolutional neural network architecture that is able to learn to compute dense disparity maps directly from the stereo inputs... (read more)

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