  Recent developments in the field of deep learning for 3D data have
demonstrated promising potential for end-to-end learning directly from point
clouds. However, many real-world point clouds contain a large class im-balance
due to the natural class im-balance observed in nature. For example, a 3D scan
of an urban environment will consist mostly of road and facade, whereas other
objects such as poles will be under-represented. In this paper we address this
issue by employing a weighted augmentation to increase classes that contain
fewer points. By mitigating the class im-balance present in the data we
demonstrate that a standard PointNet++ deep neural network can achieve higher
performance at inference on validation data. This was observed as an increase
of F1 score of 19% and 25% on two test benchmark datasets; ScanNet and
Semantic3D respectively where no class im-balance pre-processing had been
performed. Our networks performed better on both highly-represented and
under-represented classes, which indicates that the network is learning more
robust and meaningful features when the loss function is not overly exposed to
only a few classes.
