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Diffusion-Based Point Cloud Super-Resolution for mmWave Radar Data
April 10, 2024, 4:45 a.m. | Kai Luan, Chenghao Shi, Neng Wang, Yuwei Cheng, Huimin Lu, Xieyuanli Chen
cs.CV updates on arXiv.org arxiv.org
Abstract: The millimeter-wave radar sensor maintains stable performance under adverse environmental conditions, making it a promising solution for all-weather perception tasks, such as outdoor mobile robotics. However, the radar point clouds are relatively sparse and contain massive ghost points, which greatly limits the development of mmWave radar technology. In this paper, we propose a novel point cloud super-resolution approach for 3D mmWave radar data, named Radar-diffusion. Our approach employs the diffusion model defined by mean-reverting stochastic …
abstract arxiv cloud cs.cv cs.ro data development diffusion environmental ghost however making massive mobile perception performance radar resolution robotics sensor solution tasks technology type weather
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