The robustness of 3D perception systems under natural corruptions from environments and sensors is pivotal for safety-critical applications. Existing large-scale 3D perception datasets often contain data that are meticulously cleaned. Such configurations, however, cannot reflect the reliability of perception models during the deployment stage. In this work, we present Robo3D, the first comprehensive benchmark heading toward probing the robustness of 3D detectors and segmentors under out-of-distribution scenarios against natural corruptions that occur in real-world environments. Specifically, we consider eight corruption types stemming from adversarial weather conditions, external disturbances, and internal sensor failure. We uncover that, although promising results have been progressively achieved on standard benchmarks, state-of-the-art 3D perception models are at risk of being vulnerable to corruptions. We draw key observations on the use of data representations, augmentation schemes, and training strategies, that could severely affect the model's performance. To pursue better robustness, we propose a density-insensitive training framework along with a simple flexible voxelization strategy to enhance the model resiliency. We hope our benchmark and approach could inspire future research in designing more robust and reliable 3D perception models. Our robustness benchmark suite is publicly available.

翻译：在环境和传感器自然干扰下，3D感知系统的鲁棒性对安全关键应用至关重要。现有大规模3D感知数据集通常包含经过细致清洗的数据，但这种配置无法反映感知模型在部署阶段的可靠性。本文提出Robo3D——首个系统评估3D检测器与分割器在真实环境自然干扰导致的分布外场景下鲁棒性的综合基准。具体而言，我们考虑了由恶劣天气条件、外部扰动和内部传感器故障引发的八种干扰类型。研究揭示，尽管标准基准测试已逐步取得可喜成果，但当前最先进的3D感知模型仍面临易受干扰影响的隐患。我们通过关键观察发现，数据表示方式、增强策略和训练方法会显著影响模型性能。为追求更高鲁棒性，我们提出密度不敏感训练框架及简单灵活的体素化策略以增强模型韧性。期望本基准与方案能激发未来设计更鲁棒、更可靠3D感知模型的研究。本鲁棒性基准套件已公开提供。