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。