  This paper introduces PanoRadar, a novel RF imaging system that brings RF
resolution close to that of LiDAR, while providing resilience against
conditions challenging for optical signals. Our LiDAR-comparable 3D imaging
results enable, for the first time, a variety of visual recognition tasks at
radio frequency, including surface normal estimation, semantic segmentation,
and object detection. PanoRadar utilizes a rotating single-chip mmWave radar,
along with a combination of novel signal processing and machine learning
algorithms, to create high-resolution 3D images of the surroundings. Our system
accurately estimates robot motion, allowing for coherent imaging through a
dense grid of synthetic antennas. It also exploits the high azimuth resolution
to enhance elevation resolution using learning-based methods. Furthermore,
PanoRadar tackles 3D learning via 2D convolutions and addresses challenges due
to the unique characteristics of RF signals. Our results demonstrate
PanoRadar's robust performance across 12 buildings.
