DREAM-PCD: mmWaveレーダーポイントクラウドの深層再構成と強調
DREAM-PCD: Deep Reconstruction and Enhancement of mmWave Radar Pointcloud
信号処理と深層学習を組み合わせ、鏡面反射損失・低角度分解能・ノイズ干渉という3課題を解決するmmWaveレーダーポイントクラウド再構成フレームワークを提案し、大規模屋内データセットRadarEyesを構築した。
著者: Ruixu Geng, Yadong Li, Dongheng Zhang, Jincheng Wu, Yating Gao, Yang Hu, Yan Chen
分類: eess.IV, cs.RO
原文アブストラクト
Millimeter-wave (mmWave) radar pointcloud offers attractive potential for 3D sensing, thanks to its robustness in challenging conditions such as smoke and low illumination. However, existing methods failed to simultaneously address the three main challenges in mmWave radar pointcloud reconstruction: specular information lost, low angular resolution, and strong interference and noise. In this paper, we propose DREAM-PCD, a novel framework that combines signal processing and deep learning methods into three well-designed components to tackle all three challenges: Non-Coherent Accumulation for dense points, Synthetic Aperture Accumulation for improved angular resolution, and Real-Denoise Multiframe network for noise and interference removal. Moreover, the causal multiframe and "real-denoise" mechanisms in DREAM-PCD significantly enhance the generalization performance. We also introduce RadarEyes, the largest mmWave indoor dataset with over 1,000,000 frames, featuring a unique design incorporating two orthogonal single-chip radars, lidar, and camera, enriching dataset diversity and applications. Experimental results demonstrate that DREAM-PCD surpasses existing methods in reconstruction quality, and exhibits superior generalization and real-time capabilities, enabling high-quality real-time reconstruction of radar pointcloud under various parameters and scenarios. We believe that DREAM-PCD, along with the RadarEyes dataset, will significantly advance mmWave radar perception in future real-world applications.
