  As 3D object detection on point clouds relies on the geometrical
relationships between the points, non-standard object shapes can hinder a
method's detection capability. However, in safety-critical settings, robustness
to out-of-domain and long-tail samples is fundamental to circumvent dangerous
issues, such as the misdetection of damaged or rare cars. In this work, we
substantially improve the generalization of 3D object detectors to
out-of-domain data by deforming point clouds during training. We achieve this
with 3D-VField: a novel data augmentation method that plausibly deforms objects
via vector fields learned in an adversarial fashion. Our approach constrains 3D
points to slide along their sensor view rays while neither adding nor removing
any of them. The obtained vectors are transferable, sample-independent and
preserve shape and occlusions. Despite training only on a standard dataset,
such as KITTI, augmenting with our vector fields significantly improves the
generalization to differently shaped objects and scenes. Towards this end, we
propose and share CrashD: a synthetic dataset of realistic damaged and rare
cars, with a variety of crash scenarios. Extensive experiments on KITTI, Waymo,
our CrashD and SUN RGB-D show the generalizability of our techniques to
out-of-domain data, different models and sensors, namely LiDAR and ToF cameras,
for both indoor and outdoor scenes. Our CrashD dataset is available at
https://crashd-cars.github.io.
