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.

使用卷积神经网络中的 PWOC-3D 架构来解决场景流问题，该架构使用专门的设计决策来处理大运动和遮挡等挑战，并提出了一种自监督策略来预测图像中的遮挡，从而在 KITTI 基准测试和具有挑战性的 FlyingThings3D 数据集上实现了具有竞争力的结果。