# Incorporating Human Domain Knowledge in 3D LiDAR-based Semantic Segmentation

## TL;DR

This paper introduces a novel approach for 3D LiDAR semantic segmentation that incorporates human domain knowledge into neural networks, reducing manual annotation needs and enhancing training efficiency.

## Contribution

It proposes a method to embed human knowledge via pretraining with rule-based classifier samples, improving performance with fewer annotations.

## Key findings

- Pretrained models outperform randomly initialized ones.
- Our method achieves comparable accuracy with fewer manual annotations.
- Incorporating human knowledge enhances training efficiency.

## Abstract

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.