Rene Schuster

"Advances in 3D Motion Estimation for Driving Scenarios"

Intelligent vehicles for assisted and autonomous driving will define the future of transportation. Precise visual perception is a key challenge to enable these technologies. Estimation of the motion of the environment is one of the important core components of autonomous vehicles and highly assisted driving, e.g. to avoid collisions or to predict the actions of other traffic participants. The image-based full 6D estimation of 3D geometry and 3D motion is known as the scene flow problem. Scene flow provides a detailed and powerful representation of the environment. In state-of-the-art, due to its high complexity, scene flow is often replaced by less-dimensional motion representations (e.g. optical flow). However, under special assumptions or controlled indoor environments, some existing scene flow algorithms can achieve impressive results. This work is embedded in the conflicting field of speed, robustness, and accuracy to push the limits of 3D motion estimation, especially in the context of traffic. The efforts are centered around the following questions: How to transfer successful concepts of 2D optical flow estimation to the more complex scene flow problem? Can deep learning improve scene flow estimation, despite very limited availability of data?