  Understanding the flow in 3D space of sparsely sampled points between two
consecutive time frames is the core stone of modern geometric-driven systems
such as VR/AR, Robotics, and Autonomous driving. The lack of real,
non-simulated, labeled data for this task emphasizes the importance of self- or
un-supervised deep architectures. This work presents a new self-supervised
training method and an architecture for the 3D scene flow estimation under
occlusions. Here we show that smart multi-layer fusion between flow prediction
and occlusion detection outperforms traditional architectures by a large margin
for occluded and non-occluded scenarios. We report state-of-the-art results on
Flyingthings3D and KITTI datasets for both the supervised and self-supervised
training.

Computer Vision and Pattern Recognition