  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.
