  State-of-the-art approaches for the semantic labeling of LiDAR point clouds
heavily rely on the use of deep Convolutional Neural Networks (CNNs). However,
transferring network architectures across different LiDAR sensor types
represents a significant challenge, especially due to sensor specific design
choices with regard to network architecture as well as data representation. In
this paper we propose a new CNN architecture for the point-wise semantic
labeling of LiDAR data which achieves state-of-the-art results while increasing
portability across sensor types. This represents a significant advantage given
the fast-paced development of LiDAR hardware technology. We perform a thorough
quantitative cross-sensor analysis of semantic labeling performance in
comparison to a state-of-the-art reference method. Our evaluation shows that
the proposed architecture is indeed highly portable, yielding an improvement of
10 percentage points in the Intersection-over-Union (IoU) score when compared
to the reference approach. Further, the results indicate that the proposed
network architecture can provide an efficient way for the automated generation
of large-scale training data for novel LiDAR sensor types without the need for
extensive manual annotation or multi-modal label transfer.
