DREAM-PCD: Deep Reconstruction and Enhancement of mmWave Radar Pointcloud

Ruixu Geng, Yadong Li, Dongheng Zhang, Jincheng Wu, Yating Gao, Yang Hu, Yan Chen

Abstract: 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 severe interference. In this paper, we propose DREAM-PCD, a novel framework specifically designed for real-time 3D environment sensing 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 interference removal. By leveraging causal multiple viewpoints accumulation and the “real-denoise” mechanism, DREAM-PCD significantly enhances the generalization performance and real-time capability. 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.