  Semantic understanding of the surrounding environment is essential for
automated vehicles. The recent publication of the SemanticKITTI dataset
stimulates the research on semantic segmentation of LiDAR point clouds in urban
scenarios. While most existing approaches predict sparse pointwise semantic
classes for the sparse input LiDAR scan, we propose PillarSegNet to be able to
output a dense semantic grid map. In contrast to a previously proposed grid map
method, PillarSegNet uses PointNet to learn features directly from the 3D point
cloud and then conducts 2D semantic segmentation in the top view. To train and
evaluate our approach, we use both sparse and dense ground truth, where the
dense ground truth is obtained from multiple superimposed scans. Experimental
results on the SemanticKITTI dataset show that PillarSegNet achieves a
performance gain of about 10% mIoU over the state-of-the-art grid map method.
