  Despite significant progress in image-based 3D scene flow estimation, the
performance of such approaches has not yet reached the fidelity required by
many applications. Simultaneously, these applications are often not restricted
to image-based estimation: laser scanners provide a popular alternative to
traditional cameras, for example in the context of self-driving cars, as they
directly yield a 3D point cloud. In this paper, we propose to estimate 3D
motion from such unstructured point clouds using a deep neural network. In a
single forward pass, our model jointly predicts 3D scene flow as well as the 3D
bounding box and rigid body motion of objects in the scene. While the prospect
of estimating 3D scene flow from unstructured point clouds is promising, it is
also a challenging task. We show that the traditional global representation of
rigid body motion prohibits inference by CNNs, and propose a translation
equivariant representation to circumvent this problem. For training our deep
network, a large dataset is required. Because of this, we augment real scans
from KITTI with virtual objects, realistically modeling occlusions and
simulating sensor noise. A thorough comparison with classic and learning-based
techniques highlights the robustness of the proposed approach.
