Synthetic railroad level crossing point clouds
The purpose of this research is to explore methods for generating synthetic point clouds that can be used to train neural networks. This is tested using level crossings between roads and railroads. The original point clouds were captured by railborne mobile laser scanning and the 3D mesh geometries come from an object library provided by the Swedish Transport Administration. The findings show that a neural network trained using point clouds created by rearranging objects into new scenes outperforms a network trained using conventional scene augmentation. Networks trained using the semi- and fully synthetic point clouds do not reach the baseline of conventional augmentation, but there could still be benefits to the approach. The main challenges of synthesizing point clouds consists of creating appropriate radiometric profiles and creating sufficient geometric variation for the object geometries. This repository contains the code and the point cloud samples used to generate the data sets used in the study. The data consists of the original point clouds broken down into individual objects and point clouds created from 3D mesh geometries. The code is used to assemble ground truth data, create augmented copies of the original crossings, generate new crossings by semi-randomly assembling point cloud components, and creating synthetic crossings using geometries from 3D mesh models. For information about how to use the code, see readme.md.