AdvMono3D: Advanced Monocular 3D Object Detection with Depth-Aware Robust Adversarial Training

Understand

Monocular 3D object detection plays a pivotal role in the field of autonomous driving and numerous deep learning-based methods have made significant breakthroughs in this area.

• Despite the advancements in detection accuracy and efficiency, these models tend to fail when faced with such attacks, rendering them ineffective.
• Therefore, bolstering the adversarial robustness of 3D detection models has become a crucial issue that demands immediate attention and innovative solutions.
• To mitigate this issue, we propose a depth-aware robust adversarial training method for monocular 3D object detection, dubbed DART3D.