Depth estimation for advancing intelligent transport systems based on self-improving pyramid stereo network

In autonomous driving, stereo vision-based depth estimation technology can help to estimate the distance of obstacles accurately, which is crucial for correctly planning the path of the vehicle. Recent work has formulated the stereo depth estimation problem into a deep learning model with convolutional neural networks. However, these methods need a lot of post-processing and do not have strong adaptive capabilities to ill-posed regions or new scenes. In addition, due to the difficulty of the labelling the ground truth depth for real circumstance, training data for the system is limited. To overcome the above problems, the authors came up with self-improving pyramid stereo network, which can not only get a direct regression disparity without complicated post-processing but also be robust in ill-posed area. Moreover, by online learning, the proposed model can not only address the data limitation problem but also save the time spent on training and hardware resources in practice. At the same time, the proposed model has a self-improving ability to new scenes, which can quickly adjust the model according to the test data in time and improve the accuracy of prediction. Experiments on Scene Flow and KITTI data set demonstrate the effectiveness of the proposed network.