  Deep learning-based monocular depth estimation (MDE), extensively applied in
autonomous driving, is known to be vulnerable to adversarial attacks. Previous
physical attacks against MDE models rely on 2D adversarial patches, so they
only affect a small, localized region in the MDE map but fail under various
viewpoints. To address these limitations, we propose 3D Depth Fool
(3D$^2$Fool), the first 3D texture-based adversarial attack against MDE models.
3D$^2$Fool is specifically optimized to generate 3D adversarial textures
agnostic to model types of vehicles and to have improved robustness in bad
weather conditions, such as rain and fog. Experimental results validate the
superior performance of our 3D$^2$Fool across various scenarios, including
vehicles, MDE models, weather conditions, and viewpoints. Real-world
experiments with printed 3D textures on physical vehicle models further
demonstrate that our 3D$^2$Fool can cause an MDE error of over 10 meters.
