A self-improving pyramid stereo network for intelligent transport systems

In autonomous driving, stereo vision-based depth estimation technology can help to accurately estimate the distance of obstacles, which is crucial for correct path planning of the vehicle. The stereo depth estimation problem has been formulated into a deep learning model with convolutional neural networks. However, these models 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 labeling the true ground depth for real circumstances, training data for the system is limited. A research team led by Dr. Zhang Qieshi from the Shenzhen Institutes of Advanced Technology (SIAT) of the Chinese Academy of Sciences has proposed a new technical solution to address the current depth estimation for autonomous driving.