  In the last few years, convolutional neural networks (CNNs) have demonstrated
increasing success at learning many computer vision tasks including dense
estimation problems such as optical flow and stereo matching. However, the
joint prediction of these tasks, called scene flow, has traditionally been
tackled using slow classical methods based on primitive assumptions which fail
to generalize. The work presented in this paper overcomes these drawbacks
efficiently (in terms of speed and accuracy) by proposing PWOC-3D, a compact
CNN architecture to predict scene flow from stereo image sequences in an
end-to-end supervised setting. Further, large motion and occlusions are
well-known problems in scene flow estimation. PWOC-3D employs specialized
design decisions to explicitly model these challenges. In this regard, we
propose a novel self-supervised strategy to predict occlusions from images
(learned without any labeled occlusion data). Leveraging several such
constructs, our network achieves competitive results on the KITTI benchmark and
the challenging FlyingThings3D dataset. Especially on KITTI, PWOC-3D achieves
the second place among end-to-end deep learning methods with 48 times fewer
parameters than the top-performing method.
