Conventional sensor systems record information about directly visible
objects, whereas occluded scene components are considered lost in the
measurement process. Non-line-of-sight (NLOS) methods try to recover such
hidden objects from their indirect reflections - faint signal components,
traditionally treated as measurement noise. Existing NLOS approaches struggle
to record these low-signal components outside the lab, and do not scale to
large-scale outdoor scenes and high-speed motion, typical in automotive
scenarios. In particular, optical NLOS capture is fundamentally limited by the
quartic intensity falloff of diffuse indirect reflections. In this work, we
depart from visible-wavelength approaches and demonstrate detection,
classification, and tracking of hidden objects in large-scale dynamic
environments using Doppler radars that can be manufactured at low-cost in
series production. To untangle noisy indirect and direct reflections, we learn
from temporal sequences of Doppler velocity and position measurements, which we
fuse in a joint NLOS detection and tracking network over time. We validate the
approach on in-the-wild automotive scenes, including sequences of parked cars
or house facades as relay surfaces, and demonstrate low-cost, real-time NLOS in
dynamic automotive environments.

该研究使用多普勒雷达技术进行非直达测距，利用时序多普勒速度和位置信息进行 NLOS 检测与跟踪，成功实现对动态环境中隐藏物体的检测、分类和追踪，适用于大规模的汽车场景且成本低廉。