  Scene flow estimation has been receiving increasing attention for 3D
environment perception. Monocular scene flow estimation -- obtaining 3D
structure and 3D motion from two temporally consecutive images -- is a highly
ill-posed problem, and practical solutions are lacking to date. We propose a
novel monocular scene flow method that yields competitive accuracy and
real-time performance. By taking an inverse problem view, we design a single
convolutional neural network (CNN) that successfully estimates depth and 3D
motion simultaneously from a classical optical flow cost volume. We adopt
self-supervised learning with 3D loss functions and occlusion reasoning to
leverage unlabeled data. We validate our design choices, including the proxy
loss and augmentation setup. Our model achieves state-of-the-art accuracy among
unsupervised/self-supervised learning approaches to monocular scene flow, and
yields competitive results for the optical flow and monocular depth estimation
sub-tasks. Semi-supervised fine-tuning further improves the accuracy and yields
promising results in real-time.
