An Empirical Study on Knowledge Transfer under Domain and Label Shifts in 3D LiDAR Point Clouds

Abstract

For 3D perception systems to be practical in real-world applications -- from autonomous driving to embodied AI -- models must adapt to continuously evolving object definitions and sensor domains. Yet, research on continual and transfer learning in 3D point cloud perception remains underexplored compared to 2D vision -- particularly under simultaneous domain and label shifts. To address this gap, we propose the RObust Autonomous driving under Dataset shifts (ROAD) benchmark, a comprehensive evaluation suite for LiDAR-based object classification that explicitly accounts for domain shifts as well as three key forms of label evolution: class split, class expansion, and class insertion. Using large-scale datasets (Waymo, NuScenes, Argoverse2), we evaluate zero-shot transfer, linear probe, and CL, and analyze the impact of backbone architectures, training objectives, and CL methods. Our findings reveal limitations of existing approaches under realistic shifts and establish strong baselines for future research in robust 3D perception.