  Estimating the 3D motion of points in a scene, known as scene flow, is a core
problem in computer vision. Traditional learning-based methods designed to
learn end-to-end 3D flow often suffer from poor generalization. Here we present
a recurrent architecture that learns a single step of an unrolled iterative
alignment procedure for refining scene flow predictions. Inspired by classical
algorithms, we demonstrate iterative convergence toward the solution using
strong regularization. The proposed method can handle sizeable temporal
deformations and suggests a slimmer architecture than competitive all-to-all
correlation approaches. Trained on FlyingThings3D synthetic data only, our
network successfully generalizes to real scans, outperforming all existing
methods by a large margin on the KITTI self-supervised benchmark.

Computer Vision and Pattern Recognition;Machine Learning