As Computer Vision-powered autonomous systems are increasingly deployed to solve problems in the wild, the case is made for developing visual understanding methods that are robust and flexible.
One of the most challenging tasks for this purpose is given by the extraction of scene flow, that is the dense three-dimensional vector field that associates each world point with its corresponding position in the next observed frame, hence describing its three-dimensional motion entirely. The recent addition of a limited amount of ground truth scene flow information to the popular KITTI dataset prompted a renewed interest in the study of techniques for scene flow inference, although the proposed solutions in literature mostly rely on computation-intensive techniques and are characterised by execution times that are not suited for real-time application.
In the wake of the recent widespread adoption of Deep Learning techniques to Computer Vision tasks and in light of the convenience of Unsupervised Learning for scenarios in which ground truth collection is difficult and time-consuming, this thesis work proposes the first neural network architecture to be trained in end-to-end fashion for unsupervised scene flow regression from monocular visual data, called Pantaflow. The proposed solution is much faster than currently available state-of-the-art methods and therefore represents a step towards the achievement of real-time scene flow inference
