Bridging the Resolution Gap: Cost-Effective Human Point Cloud Generation via Low-Bandwidth mmWave Radar

mmWave radar has emerged as a promising technology for human sensing. While large bandwidths and multiple antennas enable high-quality point clouds for sensing applications, low-cost 24 GHz radars are restricted to 250 MHz bandwidth by regulations, leading to limited range resolution. In addition, the limited number of antennas, constrained by cost and device size, results in low angular resolution. Together, these limitations produce sparse point clouds that impede fine-grained human sensing. In this work, we present RaPoint, the first system to produce high-density point clouds from 24 GHz radars under regulatory bandwidth limits. RaPoint introduces a joint super-resolution modeling framework that leverages the MUSIC algorithm to jointly exploit super-resolution range and angle dimensions, together with the Doppler dimension, for distinguishing reflection points. To overcome the high computational complexity problem of the MUSIC algorithm, we convert the eigenvalue decomposition from the noise subspace to the sparse signal subspace, drastically reducing computation. We implement RaPoint on three representative mmWave radars. Extensive experiments demonstrate that RaPoint improves point cloud density by 9.45× and 24.10× over the state-of-the-art research and industrial baselines, respectively, and substantially boosts downstream sensing performance. We believe RaPoint takes a significant step toward fine-grained human sensing with low-cost, bandwidth-limited 24 GHz radars, enabling affordable and ubiquitous radar sensing.