  In recent years, deep neural networks showed their exceeding capabilities in
addressing many computer vision tasks including scene flow prediction. However,
most of the advances are dependent on the availability of a vast amount of
dense per pixel ground truth annotations, which are very difficult to obtain
for real life scenarios. Therefore, synthetic data is often relied upon for
supervision, resulting in a representation gap between the training and test
data. Even though a great quantity of unlabeled real world data is available,
there is a huge lack in self-supervised methods for scene flow prediction.
Hence, we explore the extension of a self-supervised loss based on the Census
transform and occlusion-aware bidirectional displacements for the problem of
scene flow prediction. Regarding the KITTI scene flow benchmark, our method
outperforms the corresponding supervised pre-training of the same network and
shows improved generalization capabilities while achieving much faster
convergence.
