  Scene flow is a challenging task aimed at jointly estimating the 3D structure
and motion of the sensed environment. Although deep learning solutions achieve
outstanding performance in terms of accuracy, these approaches divide the whole
problem into standalone tasks (stereo and optical flow) addressing them with
independent networks. Such a strategy dramatically increases the complexity of
the training procedure and requires power-hungry GPUs to infer scene flow
barely at 1 FPS. Conversely, we propose DWARF, a novel and lightweight
architecture able to infer full scene flow jointly reasoning about depth and
optical flow easily and elegantly trainable end-to-end from scratch. Moreover,
since ground truth images for full scene flow are scarce, we propose to
leverage on the knowledge learned by networks specialized in stereo or flow,
for which much more data are available, to distill proxy annotations.
Exhaustive experiments show that i) DWARF runs at about 10 FPS on a single
high-end GPU and about 1 FPS on NVIDIA Jetson TX2 embedded at KITTI resolution,
with moderate drop in accuracy compared to 10x deeper models, ii) learning from
many distilled samples is more effective than from the few, annotated ones
available. Code available at:
https://github.com/FilippoAleotti/Dwarf-Tensorflow
