  We propose a data-driven scene flow estimation algorithm exploiting the
observation that many 3D scenes can be explained by a collection of agents
moving as rigid bodies. At the core of our method lies a deep architecture able
to reason at the \textbf{object-level} by considering 3D scene flow in
conjunction with other 3D tasks. This object level abstraction, enables us to
relax the requirement for dense scene flow supervision with simpler binary
background segmentation mask and ego-motion annotations. Our mild supervision
requirements make our method well suited for recently released massive data
collections for autonomous driving, which do not contain dense scene flow
annotations. As output, our model provides low-level cues like pointwise flow
and higher-level cues such as holistic scene understanding at the level of
rigid objects. We further propose a test-time optimization refining the
predicted rigid scene flow. We showcase the effectiveness and generalization
capacity of our method on four different autonomous driving datasets. We
release our source code and pre-trained models under
\url{github.com/zgojcic/Rigid3DSceneFlow}.

Computer Vision and Pattern Recognition;Artificial Intelligence;Machine Learning;Robotics