Diffradar: High-Quality Mmwave Radar Perception With Diffusion Probabilistic Model
Jincheng Wu, Ruixu Geng, Yadong Li, Dongheng Zhang, Zhi Lu, Yang Hu, Yan Chen
Abstract
Millimeter-wave (mmWave) radar has gained increasing attention in environmental perception due to its robustness under low-light conditions. However, existing methods fail to address the challenges of multipath interference and low angle resolution. In this paper, we introduce DiffRadar which leverages the diffusion probabilistic model (DPM) for high-quality mmWave environmental sensing. To adapt DPM for radar signals that lack pix-level structural information, we design a contour encoder to capture intrinsic scene features that enable the DPM to learn a robust representation from radar data. Then the DPM decoder utilizes this high-level semantic information to effectively reconstruct real-world scene distribution. Extensive experiments have demonstrated that our approach surpasses state-of-the-art methods in various complex scenarios.
BibTeX
@inproceedings{icassp2024_diffradarhighqua,
title = {Diffradar: High-Quality Mmwave Radar Perception With Diffusion Probabilistic Model},
author = {Jincheng Wu and Ruixu Geng and Yadong Li and Dongheng Zhang and Zhi Lu and Yang Hu and Yan Chen},
booktitle = {ICASSP 2024},
year = {2024}
}