Learning End-To-End Scene Flow by Distilling Single Tasks Knowledge

Understand

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.