  Mobile robots and autonomous vehicles rely on multi-modal sensor setups to
perceive and understand their surroundings. Aside from cameras, LiDAR sensors
represent a central component of state-of-the-art perception systems. In
addition to accurate spatial perception, a comprehensive semantic understanding
of the environment is essential for efficient and safe operation. In this paper
we present a novel deep neural network architecture called LiLaNet for
point-wise, multi-class semantic labeling of semi-dense LiDAR data. The network
utilizes virtual image projections of the 3D point clouds for efficient
inference. Further, we propose an automated process for large-scale cross-modal
training data generation called Autolabeling, in order to boost semantic
labeling performance while keeping the manual annotation effort low. The
effectiveness of the proposed network architecture as well as the automated
data generation process is demonstrated on a manually annotated ground truth
dataset. LiLaNet is shown to significantly outperform current state-of-the-art
CNN architectures for LiDAR data. Applying our automatically generated
large-scale training data yields a boost of up to 14 percentage points compared
to networks trained on manually annotated data only.
