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}.

利用物体水平推理，我们提出了一种数据驱动的场景流估计算法，适用于近期发布的无人驾驶大规模数据集。我们的方法能够提供从点流到刚性物体的全局场景理解的低级和高级线索，并在四个不同的无人驾驶数据集上展示了其有效性和泛化能力。