Hierarchical Attention Learning of Scene Flow in 3D Point Clouds

TL;DR

This paper introduces a hierarchical neural network with double attention for 3D scene flow estimation from point clouds, improving accuracy and efficiency in applications like autonomous driving.

Contribution

A novel hierarchical architecture with a more-for-less design and an attentive embedding module for enhanced 3D scene flow learning from point clouds.

Findings

Outperforms state-of-the-art on FlyingThings3D and KITTI datasets.

Effective in LiDAR odometry for autonomous driving.

Balances resource use and precision through hierarchical design.

Abstract

Scene flow represents the 3D motion of every point in the dynamic environments. Like the optical flow that represents the motion of pixels in 2D images, 3D motion representation of scene flow benefits many applications, such as autonomous driving and service robot. This paper studies the problem of scene flow estimation from two consecutive 3D point clouds. In this paper, a novel hierarchical neural network with double attention is proposed for learning the correlation of point features in adjacent frames and refining scene flow from coarse to fine layer by layer. The proposed network has a new more-for-less hierarchical architecture. The more-for-less means that the number of input points is greater than the number of output points for scene flow estimation, which brings more input information and balances the precision and resource consumption. In this hierarchical architecture, scene flow of different levels is generated and supervised respectively. A novel attentive embedding module is introduced to aggregate the features of adjacent points using a double attention method in a patch-to-patch manner. The proper layers for flow embedding and flow supervision are carefully considered in our network designment. Experiments show that the proposed network outperforms the state-of-the-art performance of 3D scene flow estimation on the FlyingThings3D and KITTI Scene Flow 2015 datasets. We also apply the proposed network to realistic LiDAR odometry task, which is an key problem in autonomous driving. The experiment results demonstrate that our proposed network can outperform the ICP-based method and shows the good practical application ability.