Proceedings of the 2024 7th International Conference on Civil Architecture, Hydropower and Engineering Management (CAHEM 2024)

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Enhancing Point Cloud Segmentation of Chinese Historical Buildings with Synthetic Data

Authors
Jiangping Ma1, 2, 3, Zhiyuan Guo1, 2, 3, Wenpeng Li1, 2, 3, Weiya Chen1, 2, 3, *
1School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
2National Center of Technology Innovation for Digital Construction, Wuhan, 430074, China
3International Joint Research Laboratory of Smart Construction, Wuhan, 430074, China
*Corresponding author. Email: weiya_chen@hust.edu.cn
Corresponding Author
Weiya Chen
Available Online 31 January 2025.
DOI
10.2991/978-94-6463-650-5_15How to use a DOI?
Keywords
Historical buildings; Point cloud semantic segmentation; Synthetic dataset
Abstract

Considering the challenges of segmenting architectural components in point cloud data, particularly for Chinese historical buildings, we develop an efficient method for constructing datasets to enhance deep learning techniques for precise segmentation. Surface sampling is integrated with advanced virtual laser scanning technology in our approach. Initially, labeled point cloud data is captured through surface sampling. Subsequently, the HELIOS++ simulation platform mimics real-world scanning to generate unlabeled data resembling actual point clouds. Precise alignment and label transfer between these two types of data result in an annotated dataset that preserves authentic scanning characteristics. Additionally, we introduce a symmetry-axis-based point cloud completion technique to address data loss during scanning, leveraging the inherent symmetry found in Chinese historical buildings. To validate the effectiveness of our dataset, two state-of-the-art deep learning models are selected for comprehensive evaluation. Experimental results demonstrate that our dataset supports efficient and stable model training, exhibits strong generalization capabilities, and provides a robust foundation for semantic segmentation of historical buildings.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2024 7th International Conference on Civil Architecture, Hydropower and Engineering Management (CAHEM 2024)
Series
Advances in Engineering Research
Publication Date
31 January 2025
ISBN
978-94-6463-650-5
ISSN
2352-5401
DOI
10.2991/978-94-6463-650-5_15How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Jiangping Ma
AU  - Zhiyuan Guo
AU  - Wenpeng Li
AU  - Weiya Chen
PY  - 2025
DA  - 2025/01/31
TI  - Enhancing Point Cloud Segmentation of Chinese Historical Buildings with Synthetic Data
BT  - Proceedings of the 2024 7th International Conference on Civil Architecture, Hydropower and Engineering Management (CAHEM 2024)
PB  - Atlantis Press
SP  - 147
EP  - 154
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-650-5_15
DO  - 10.2991/978-94-6463-650-5_15
ID  - Ma2025
ER  -