3D object detectors are fundamental components of perception systems in autonomous vehicles. While these detectors achieve remarkable performance on standard autonomous driving benchmarks, they often struggle to generalize across different domains - for instance, a model trained in the U.S. may perform poorly in regions like Asia or Europe. This paper presents a novel lidar domain adaptation method based on neuron activation patterns, demonstrating that state-of-the-art performance can be achieved by annotating only a small, representative, and diverse subset of samples from the target domain if they are correctly selected. The proposed approach requires very small annotation budget and, when combined with post-training techniques inspired by continual learning prevent weight drift from the original model. Empirical evaluation shows that the proposed domain adaptation approach outperforms both linear probing and state-of-the-art domain adaptation techniques.

翻译：三维目标检测器是自动驾驶车辆感知系统的核心组件。尽管这些检测器在标准自动驾驶基准测试中取得了显著性能，但它们在不同领域间的泛化能力往往不足——例如，在美国训练的模型在亚洲或欧洲等地区可能表现不佳。本文提出了一种基于神经元激活模式的激光雷达域自适应新方法，证明若能正确选择目标域中具有代表性且多样性的小规模样本子集进行标注，即可实现最先进的性能。所提方法所需的标注成本极低，并且结合受持续学习启发的后训练技术，可防止模型权重偏离原始模型。实证评估表明，所提出的域自适应方法在线性探测和最先进的域自适应技术中均表现出更优性能。