PointFlowHop is based on the Green Learning paradigm and decomposes 3D scene flow estimation into vehicle ego-motion compensation and object-wise motion estimation steps.
[arXiv]
PointFlowHop takes two consecutive point clouds and determines the 3D flow vectors for every point in the first point cloud. PointFlowHop decomposes the scene flow estimation task into a set of subtasks, including ego-motion compensation, object association and object-wise motion estimation. It follows the green learning (GL) pipeline and adopts the feedforward data processing path. As a result, its underlying mechanism is more transparent than deep-learning (DL) solutions based on end-to-end optimization of network parameters. We conduct experiments on the stereoKITTI and the Argoverse LiDAR point cloud datasets and demonstrate that PointFlowHop outperforms deep-learning methods with a small model size and less training time. Furthermore, we compare the Floating Point Operations (FLOPs) required by PointFlowHop and other learning-based methods in inference, and show its big savings in computational complexity.
The code has been developed and tested in Python. The following packages need to be installed.
h5py
numpy
scipy
sklearn
open3d
pyntcloud
If you find our work useful in your research, please consider citing:
@article{kadam2023pointflowhop,
title={PointFlowHop: Green and Interpretable Scene Flow Estimation from Consecutive Point Clouds},
author={Kadam, Pranav and Gu, Jiahao and Liu, Shan and Kuo, C-C Jay},
journal={arXiv preprint arXiv:2302.14193},
year={2023}
}