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Journal of Graphics ›› 2025, Vol. 46 ›› Issue (5): 1134-1143.DOI: 10.11996/JG.j.2095-302X.2025051134

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Dimension detection method for cable support curtain wall panels based on unbalanced optimal transport theory

TAN Liyun1,2(), LIU Jiepeng1,2(), LI Hantao3, ZENG Yan1,2, LIAO Yue1,2, WU Xiaofeng3, CUI Na1,2   

  1. 1 School of Civil Engineering, Chongqing University, Chongqing 400045, China
    2 Key Laboratory of New Technology for Construction of Cities in Mountain Area, Chongqing University, Chongqing 400045, China
    3 Guangzhou Gezhouba Group Construction Engineering CO., LTD, , Guangzhou Guangdong 511466, China
  • Received:2024-11-11 Accepted:2025-04-25 Online:2025-10-30 Published:2025-09-10
  • Contact: LIU Jiepeng
  • About author:First author contact:

    TAN Liyun (2001-), master student. Her main research interest covers intelligent construction. E-mail:erintly@cqu.edu.cn

  • Supported by:
    National Natural Science Foundation of China(52130801)

Abstract:

Glass curtain walls are widely used in large venues and landmark buildings due to their unique aesthetic appeal and powerful shaping capabilities. The construction process is to first construct the keel support system and then install and debug the glass panels. However, during the specific operation, the axis of the built keel usually deviates from the design axis, complicating subsequent construction of glass panels. Our research on axis extraction of curtain-wall based on unbalanced optimal transport theory was conducted. This method fully utilized point-cloud data information and combined non-equilibrium optimal transmission theory. Steps included inputting point cloud data, preprocessing point cloud data, random sampling to obtain an initial axis point set, extracting a thick axis of the rod, and extracting a fine axis of the rod. In this way, the axis features of the target point cloud were obtained. Then, the curtain-wall dimensions were obtained based on the extracted axis. Experimental results showed that this method can effectively extract the axis of the curtain-wall keel and exhibited strong centrality and robustness. The effectiveness of the method was demonstrated by comparison with other algorithms. Compared with the actual measured results, the calculated curtain-wall dimensions deviated by within ±2 mm, remaining within the allowable error range.

Key words: point cloud data, axis extraction, unbalanced optimal transport, glass curtain wall, intelligent construction

CLC Number: