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.
Abstract (translated)
为了使三维感知系统在现实世界应用（从自动驾驶到具身人工智能）中变得实用，模型必须适应不断变化的对象定义和传感器领域。然而，在三维点云感知中的连续学习和迁移学习的研究相比二维视觉仍然相对较少，特别是在同时出现领域和标签转换的情况下。为了解决这一不足，我们提出了“ROAD”基准测试（Robust Autonomous Driving Under Dataset Shifts的简称），这是一个全面评估基于激光雷达的对象分类性能的评测套件，该套件明确考虑了领域转换以及三种关键形式的标签演变：类别分裂、类别扩展和类别插入。利用大规模数据集（Waymo、NuScenes 和 Argoverse2），我们评估了零样本迁移学习、线性探测和持续学习方法，并分析了骨干架构、训练目标和持续学习方法的影响。我们的研究结果揭示了现有方法在现实世界转换中的局限性，并为未来稳健的三维感知研究建立了强有力的基础。
URL
https://arxiv.org/abs/2601.07855
