SGV3D:Towards Scenario Generalization for Vision-based Roadside 3D Object Detection

arXiv:2401.16110v2 Announce Type: replace
Abstract: Roadside perception can greatly increase the safety of autonomous vehicles by extending their perception ability beyond the visual range and addressing blind spots. However, current state-of-the-art vision-based roadside detection methods possess high accuracy on labeled scenes but have inferior performance on new scenes. This is because roadside cameras remain stationary after installation and can only collect data from a single scene, resulting in the algorithm overfitting these roadside backgrounds and camera poses. To address this …

3d object 3d object detection arxiv cs.cv detection object type vision