  In this paper we introduce a novel way to predict semantic information from
sparse, single-shot LiDAR measurements in the context of autonomous driving. In
particular, we fuse learned features from complementary representations. The
approach is aimed specifically at improving the semantic segmentation of
top-view grid maps. Towards this goal the 3D LiDAR point cloud is projected
onto two orthogonal 2D representations. For each representation a tailored deep
learning architecture is developed to effectively extract semantic information
which are fused by a superordinate deep neural network. The contribution of
this work is threefold: (1) We examine different stages within the segmentation
network for fusion. (2) We quantify the impact of embedding different features.
(3) We use the findings of this survey to design a tailored deep neural network
architecture leveraging respective advantages of different representations. Our
method is evaluated using the SemanticKITTI dataset which provides a point-wise
semantic annotation of more than 23.000 LiDAR measurements.
