  Existing work on scene flow estimation focuses on autonomous driving and
mobile robotics, while automated solutions are lacking for motion in nature,
such as that exhibited by debris flows. We propose DEFLOW, a model for 3D
motion estimation of debris flows, together with a newly captured dataset. We
adopt a novel multi-level sensor fusion architecture and self-supervision to
incorporate the inductive biases of the scene. We further adopt a multi-frame
temporal processing module to enable flow speed estimation over time. Our model
achieves state-of-the-art optical flow and depth estimation on our dataset, and
fully automates the motion estimation for debris flows. The source code and
dataset are available at project page.
