RCP: Recurrent Closest Point for Point Cloud

Chengzhou Tang

Abstract:

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 [32] and KITTI [33] datasets.
For point cloud registration, we follow previous works and
evaluate the data pairs with large pose and partially overlapping from ModelNet40 [65]. 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. Our source code is available at
https://github.com/gxd1994/RCP.