  Autonomous Vehicles (AVs) are mostly reliant on LiDAR sensors which enable
spatial perception of their surroundings and help make driving decisions.
Recent works demonstrated attacks that aim to hide objects from AV perception,
which can result in severe consequences. 3D shadows, are regions void of
measurements in 3D point clouds which arise from occlusions of objects in a
scene. 3D shadows were proposed as a physical invariant valuable for detecting
spoofed or fake objects. In this work, we leverage 3D shadows to locate
obstacles that are hidden from object detectors. We achieve this by searching
for void regions and locating the obstacles that cause these shadows. Our
proposed methodology can be used to detect an object that has been hidden by an
adversary as these objects, while hidden from 3D object detectors, still induce
shadow artifacts in 3D point clouds, which we use for obstacle detection. We
show that using 3D shadows for obstacle detection can achieve high accuracy in
matching shadows to their object and provide precise prediction of an
obstacle's distance from the ego-vehicle.
