  This work studies semantic segmentation using 3D LiDAR data. Popular deep
learning methods applied for this task require a large number of manual
annotations to train the parameters. We propose a new method that makes full
use of the advantages of traditional methods and deep learning methods via
incorporating human domain knowledge into the neural network model to reduce
the demand for large numbers of manual annotations and improve the training
efficiency. We first pretrain a model with autogenerated samples from a
rule-based classifier so that human knowledge can be propagated into the
network. Based on the pretrained model, only a small set of annotations is
required for further fine-tuning. Quantitative experiments show that the
pretrained model achieves better performance than random initialization in
almost all cases; furthermore, our method can achieve similar performance with
fewer manual annotations.
