  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.
