  In this paper, we present a deep learning architecture which addresses the
problem of 3D semantic segmentation of unstructured point clouds. Compared to
previous work, we introduce grouping techniques which define point
neighborhoods in the initial world space and the learned feature space.
Neighborhoods are important as they allow to compute local or global point
features depending on the spatial extend of the neighborhood. Additionally, we
incorporate dedicated loss functions to further structure the learned point
feature space: the pairwise distance loss and the centroid loss. We show how to
apply these mechanisms to the task of 3D semantic segmentation of point clouds
and report state-of-the-art performance on indoor and outdoor datasets.
