  3D scene flow estimation is a vital tool in perceiving our environment given
depth or range sensors. Unlike optical flow, the data is usually sparse and in
most cases partially occluded in between two temporal samplings. Here we
propose a new scene flow architecture called OGSF-Net which tightly couples the
learning for both flow and occlusions between frames. Their coupled symbiosis
results in a more accurate prediction of flow in space. Unlike a traditional
multi-action network, our unified approach is fused throughout the network,
boosting performances for both occlusion detection and flow estimation. Our
architecture is the first to gauge the occlusion in 3D scene flow estimation on
point clouds. In key datasets such as Flyingthings3D and KITTI, we achieve the
state-of-the-art results.
