RDN: An Efficient Denoising Network for 4D Radar Point Clouds
Ningyuan Huang, Zhiheng Li, Chenglin Pang, Zheng Fang
Abstract
Accurate point cloud information is important for robot perception and autonomous driving. Although advanced 4D radar can provide point cloud with higher resolution than 3D radar, its data still contains a significant amount of noise due to measurement principle. To solve this issue, we propose RDN (Radar Denoising Network), a denoising network specifically designed for 4D radar. RDN includes three innovative modules: First, to overcome the noisy nature of radar points, we design a feature similarity-based farthest point sampling module (FS-FPS), which can extract representative sampling points from the noisy point cloud. Secondly, to address feature propagation issues caused by the sparse and long-range characteristics of 4D radar points, we introduce a virtual feature point prediction (VFP) module and an iterative upsampling (IUS) module. The VFP module generates virtual feature points through the network to serve as bridges for information transmission, while the IUS module uses an iterative approach to gradually refine feature propagation. The experiments on MSC-RAD4D and NTU4DRadLM datasets demonstrate the effectiveness and generalization of our method. Besides, odometry experiments prove the practical value of point cloud denoising in improving robot perception.
BibTeX
@inproceedings{iros2025_rdnanefficientde,
title = {RDN: An Efficient Denoising Network for 4D Radar Point Clouds},
author = {Ningyuan Huang and Zhiheng Li and Chenglin Pang and Zheng Fang},
booktitle = {IROS 2025},
year = {2025}
}