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

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Ma, Hanxin and Martens, Jan and Blankenbach, Jörg; Moreno-Rangel, Alejandro and Kumar, Bimal, eds. (2025) Generation and evaluation of synthetic point cloud data for training of machine learning models. In: EG-ICE 2025. University of Strathclyde Publishing, GBR, pp. 211-219. ISBN 9781914241826 (https://doi.org/10.17868/strath.00093286)

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Abstract

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.

  • Item type:Book Section
    ID code:93286
    Dates:
    Date
    Event
    1 July 2025
    Published
    9 June 2025
    Accepted
    Subjects:Fine Arts > Architecture
    Department:Faculty of Engineering > Architecture
    Depositing user: Pure Administrator
    Date deposited:27 Jun 2025 14:40
    Last modified:29 Jun 2026 09:40
    URI:https://strathprints.strath.ac.uk/id/eprint/93286
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