Since real-world training datasets cannot properly sample the long tail of the underlying data distribution, corner cases and rare out-of-domain samples can severely hinder the performance of state-of-the-art models. This problem becomes even more severe for dense tasks, such as 3D semantic segmentation, where points of non-standard objects can be confidently associated to the wrong class. In this work, we focus on improving the generalization to out-of-domain data. We achieve this by augmenting the training set with adversarial examples. First, we learn a set of vectors that deform the objects in an adversarial fashion. To prevent the adversarial examples from being too far from the existing data distribution, we preserve their plausibility through a series of constraints, ensuring sensor-awareness and shapes smoothness. Then, we perform adversarial augmentation by applying the learned sample-independent vectors to the available objects when training a model. We conduct extensive experiments across a variety of scenarios on data from KITTI, Waymo, and CrashD for 3D object detection, and on data from SemanticKITTI, Waymo, and nuScenes for 3D semantic segmentation. Despite training on a standard single dataset, our approach substantially improves the robustness and generalization of both 3D object detection and 3D semantic segmentation methods to out-of-domain data.

翻译：由于现实训练数据集无法充分采样底层数据分布的长尾部分，极端案例和罕见域外样本会严重削弱现有最优模型的性能。这一问题在三维语义分割等密集预测任务中尤为突出，非标准物体点云可能被高置信度地错误分类。本文聚焦于提升模型对域外数据的泛化能力，通过对抗性样本增强训练集实现这一目标。首先，我们学习一组以对抗方式形变物体的向量。为防止对抗样本过度偏离现有数据分布，我们通过一系列约束保持其合理性，确保传感器感知一致性与形状平滑性。随后，在模型训练阶段，通过将学习到的样本无关向量应用于现有物体，执行对抗增强。我们在KITTI、Waymo和CrashD数据集上进行三维目标检测，并在SemanticKITTI、Waymo和nuScenes数据集上进行三维语义分割，开展了涵盖多种场景的广泛实验。尽管仅在单一标准数据集上训练，我们的方法显著提升了三维目标检测与三维语义分割方法对域外数据的鲁棒性与泛化能力。