Towards Universal LiDAR-Based 3D Object Detection by Multi-Domain Knowledge Transfer

Abstract

Contemporary LiDAR-based 3D object detection methods mostly focus on single-domain learning or cross-domain adaptive learning. However, for autonomous driving systems, optimizing a specific LiDAR-based 3D object detector for each domain is costly and lacks of scalability in real-world deployment. It is desirable to train a universal LiDAR-based 3D object detector from multiple domains. In this work, we propose the first attempt to explore multi-domain learning and generalization for LiDAR-based 3D object detection. We show that jointly optimizing a 3D object detector from multiple domains achieves better generalization capability compared to the conventional single-domain learning model. To explore informative knowledge across domains towards a universal 3D object detector, we propose a multi-domain knowledge transfer framework with universal feature transformation. This approach leverages spatial-wise and channel-wise knowledge across domains to learn universal feature representations, so it facilitates to optimize a universal 3D object detector for deployment at different domains. Extensive experiments on four benchmark datasets (Waymo, KITTI, NuScenes and ONCE) show the superiority of our approach over the state-of-the-art approaches for multi-domain learning and generalization in LiDAR-based 3D object detection.