Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
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.
Highlights Machine learning point clouds' automation is impeded by the lack of data. Synthetic data can enhance or replace real datasets. Laser scanners simulators can generate synthetic point clouds matching real. DynamoPCSim is a visual programming scanning simulator operating on BIM models. DynamoPCSim-generated point clouds are effective for machine-learning training.
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.
Highlights Machine learning point clouds' automation is impeded by the lack of data. Synthetic data can enhance or replace real datasets. Laser scanners simulators can generate synthetic point clouds matching real. DynamoPCSim is a visual programming scanning simulator operating on BIM models. DynamoPCSim-generated point clouds are effective for machine-learning training.
Visual programming simulator for producing realistic labeled point clouds from digital infrastructure models
Korus, Kamil (Autor:in) / Czerniawski, Thomas (Autor:in) / Salamak, Marek (Autor:in)
09.10.2023
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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