  3D motion estimation including scene flow and point cloud registration has
drawn increasing interest. Inspired by 2D flow estimation, recent methods
employ deep neural networks to construct the cost volume for estimating
accurate 3D flow. However, these methods are limited by the fact that it is
difficult to define a search window on point clouds because of the irregular
data structure. In this paper, we avoid this irregularity by a simple yet
effective method.We decompose the problem into two interlaced stages, where the
3D flows are optimized point-wisely at the first stage and then globally
regularized in a recurrent network at the second stage. Therefore, the
recurrent network only receives the regular point-wise information as the
input. In the experiments, we evaluate the proposed method on both the 3D scene
flow estimation and the point cloud registration task. For 3D scene flow
estimation, we make comparisons on the widely used FlyingThings3D and
KITTIdatasets. For point cloud registration, we follow previous works and
evaluate the data pairs with large pose and partially overlapping from
ModelNet40. The results show that our method outperforms the previous method
and achieves a new state-of-the-art performance on both 3D scene flow
estimation and point cloud registration, which demonstrates the superiority of
the proposed zero-order method on irregular point cloud data.
