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

Understand

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.
• Training is performed in an end-to-end fashion without the need of ground-truth disparity maps.
• The idea is to use image warping error (instead of disparity-map residuals) as the loss function to drive the learning process, aiming to find a depth-map that minimizes the warping error.