Visual programming simulator for producing realistic labeled point clouds from digital infrastructure models

Abstract

The increasing availability of point clouds has led to intensive research into automating point cloud processing using machine learning. While supervised systems require large and diverse labeled datasets, the cost and time of manual data creation can be overcome with synthetic data. This paper introduces DynamoPCSim, a versatile scanning simulator based on visual programming, implementing ray tracing, and operating on BIM models. The simulator collects measurements of digital models and transfers the model semantic data to generated point clouds, enabling automated labeling. Customizable scanning parameters allow for the reflection of real scanners (including imperfections) and the transformation of synthetic point clouds, making the data more realistic. The evaluation of generated point clouds against real-world data through a neural network segmentation experiment provides a foundation for the effective utilization of DynamoPCSim synthetic point clouds in machine learning training.