Generation and evaluation of synthetic point cloud data for training of machine learning models

Machine learning is increasingly applied for automating the creation of Building Information Models (BIM) of infrastructure objects and for point cloud semantic segmentation. However, machine learning is often costly and time-consuming due to the substantial volume of point cloud training data required which in turn must be captured and processed. This paper proposes the use of virtual laser scanning software to generate synthetic point clouds as a cost-effective alternative to manual capturing. It evaluates the impact of training machine learning models for infrastructure semantic segmentation using a hybrid dataset of real-world and synthetic point clouds. The findings indicate that models trained on hybrid datasets markedly outperform those trained solely on real-world data. This improvement illustrates the potential of augmenting datasets with synthetic data to not only improve model accuracy but also reduce the resources needed for manual data collection and labelling.