  To train a well performing neural network for semantic segmentation, it is
crucial to have a large dataset with available ground truth for the network to
generalize on unseen data. In this paper we present novel point cloud
augmentation methods to artificially diversify a dataset. Our sensor-centric
methods keep the data structure consistent with the lidar sensor capabilities.
Due to these new methods, we are able to enrich low-value data with high-value
instances, as well as create entirely new scenes. We validate our methods on
multiple neural networks with the public SemanticKITTI dataset and demonstrate
that all networks improve compared to their respective baseline. In addition,
we show that our methods enable the use of very small datasets, saving
annotation time, training time and the associated costs.
