Understanding the Robustness of 3D Object Detection with Bird's-Eye-View Representations in Autonomous Driving

Understand

3D object detection is an essential perception task in autonomous driving to understand the environments.

• The Bird's-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with camera inputs on popular benchmarks.
• However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems.
• In this paper, we evaluate the natural and adversarial robustness of various representative models under extensive settings, to fully understand their behaviors influenced by explicit BEV features compared with those without BEV.