  mmWave radars offer excellent depth resolution even at very long ranges owing
to their high bandwidth. But their angular resolution is at least an
order-of-magnitude worse than camera and lidar systems. Hence, mmWave radar is
not a capable 3-D imaging solution in isolation. We propose Metamoran, a system
that combines the complimentary strengths of radar and camera to obtain
accurate, high resolution depth images over long ranges even in high clutter
environments, all from a single fixed vantage point. Metamoran enables rich
long-range depth imaging with applications in security and surveillance,
roadside safety infrastructure and wide-area mapping. Our approach leverages
the high angular resolution from cameras using computer vision techniques,
including image segmentation and monocular depth estimation, to obtain object
shape. Our core contribution is a method to convert this object shape into an
RF I/Q equivalent, which we use in a novel radar processing pipeline to help
declutter the scene and capture extremely weak reflections from objects at long
distances. We perform a detailed evaluation of Metamoran's depth imaging
capabilities in 400 diverse scenes. Our evaluation shows that Metamoran
estimates the depth of static objects up to 90 m and moving objects up to 305 m
and with a median error of 28 cm, an improvement of 13$\times$ compared to a
naive radar+camera baseline and 23$\times$ compared to monocular depth
estimation.

Computer Vision and Pattern Recognition;Networking and Internet Architecture;Robotics;Signal Processing