  Autonomous driving vehicles and robotic systems rely on accurate perception
of their surroundings. Scene understanding is one of the crucial components of
perception modules. Among all available sensors, LiDARs are one of the
essential sensing modalities of autonomous driving systems due to their active
sensing nature with high resolution of sensor readings. Accurate and fast
semantic segmentation methods are needed to fully utilize LiDAR sensors for
scene understanding. In this paper, we present Lite-HDSeg, a novel real-time
convolutional neural network for semantic segmentation of full $3$D LiDAR point
clouds. Lite-HDSeg can achieve the best accuracy vs. computational complexity
trade-off in SemanticKitti benchmark and is designed on the basis of a new
encoder-decoder architecture with light-weight harmonic dense convolutions as
its core. Moreover, we introduce ICM, an improved global contextual module to
capture multi-scale contextual features, and MCSPN, a multi-class Spatial
Propagation Network to further refine the semantic boundaries. Our experimental
results show that the proposed method outperforms state-of-the-art semantic
segmentation approaches which can run real-time, thus is suitable for robotic
and autonomous driving applications.
