3D Object Detectors (3D-OD) are crucial for understanding the environment in many robotic tasks, especially autonomous driving. Including 3D information via Lidar sensors improves accuracy greatly. However, such detectors perform poorly on domains they were not trained on, i.e. different locations, sensors, weather, etc., limiting their reliability in safety-critical applications. There exist methods to adapt 3D-ODs to these domains; however, these methods treat 3D-ODs as a black box, neglecting underlying architectural decisions and source-domain training strategies. Instead, we dive deep into the details of 3D-ODs, focusing our efforts on fundamental factors that influence robustness prior to domain adaptation. We systematically investigate four design choices (and the interplay between them) often overlooked in 3D-OD robustness and domain adaptation: architecture, voxel encoding, data augmentations, and anchor strategies. We assess their impact on the robustness of nine state-of-the-art 3D-ODs across six benchmarks encompassing three types of domain gaps - sensor type, weather, and location. Our main findings are: (1) transformer backbones with local point features are more robust than 3D CNNs, (2) test-time anchor size adjustment is crucial for adaptation across geographical locations, significantly boosting scores without retraining, (3) source-domain augmentations allow the model to generalize to low-resolution sensors, and (4) surprisingly, robustness to bad weather is improved when training directly on more clean weather data than on training with bad weather data. We outline our main conclusions and findings to provide practical guidance on developing more robust 3D-ODs.

通过系统调查四个在3D目标检测的鲁棒性和领域适应中常常被忽视的设计选择（架构、体素编码、数据增强和锚点策略）以及它们之间的相互作用，我们发现用本地点特征的Transformer主干比3D CNN更加鲁棒，在地理位置适应中关键是在测试时进行的锚点大小调整，源域数据增强允许模型推广到低分辨率传感器，而与预期相反，用干净的天气数据直接训练比用恶劣天气数据训练更能提高对恶劣天气的鲁棒性。我们总结了主要结论和发现，为开发更加鲁棒的3D目标检测方法提供实际指导。

激光雷达3D物体检测器对未知领域的泛化能力的实证研究