March 14, 2024, 4:46 a.m. | Ruibin Zhang, Donglai Xue, Yuhan Wang, Ruixu Geng, Fei Gao

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arXiv:2403.08460v1 Announce Type: new
Abstract: Millimeter wave (mmWave) radars have attracted significant attention from both academia and industry due to their capability to operate in extreme weather conditions. However, they face challenges in terms of sparsity and noise interference, which hinder their application in the field of micro aerial vehicle (MAV) autonomous navigation. To this end, this paper proposes a novel approach to dense and accurate mmWave radar point cloud construction via cross-modal learning. Specifically, we introduce diffusion models, which …

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