Towards synthesized training data for semantic segmentation of mobile laser scanning point clouds: Generating level crossings from real and synthetic point cloud samples

Abstract

This paper presents a method for synthesizing mobile laser scanning point clouds of railroad level crossings that can be used to train neural networks for point cloud segmentation. The method arranges point cloud samples representing individual objects into new scenes using a set of simple placement rules. The point cloud samples can be cropped from real point clouds, created from 3D mesh models, or procedurally generated using mathematical functions. The scenes can consist of one or more types of samples, making it possible to combine real and synthetic data. The findings show that a network trained on scenes generated from real point cloud samples resulted in a better overall F1-score compared to a network that was trained using real scenes. Also, the performance of a network trained on a very small amount of real scenes can be improved by adding fully synthetic scenes to the training data.