Deep Learning–Based Scan-to-BIM Automation and Object Scope Expansion Using a Low-Cost 3D Scan Data-Reference-Cited by-同舟云学术

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Deep Learning–Based Scan-to-BIM Automation and Object Scope Expansion Using a Low-Cost 3D Scan Data

Published:2024-11 Issue:6 Volume:38 Page:
ISSN:0887-3801
Container-title:Journal of Computing in Civil Engineering
language:en
Short-container-title:J. Comput. Civ. Eng.

Author:

Won Ma Jong¹^ORCID,Jung Jaehoon²^ORCID,Leite Fernanda³

Affiliation:

1. Assistant Professor, Dept. of Building, Civil, and Environmental Engineering, Concordia Univ., 1455 De Maisonneuve W., Montreal, QC, Canada H3G1M8 (corresponding author). ORCID: .

2. Assistant Professor, Dept. of Urban Engineering, Gyeongsang National Univ., 501 Jinju-daero, Jinju, Gyeongnam 52828, South Korea. ORCID: .

3. Professor and John A. Focht Centennial Teaching Fellow in Civil Engineering, Dept. of Civil, Architectural, and Environmental Engineering, Univ. of Texas at Austin, 1 University Station C1752, Austin, TX 78712-0276.

Publisher

American Society of Civil Engineers (ASCE)

Link

https://ascelibrary.org/doi/pdf/10.1061/JCCEE5.CPENG-5751

Reference91 articles.

1. CLOI-NET: Class segmentation of industrial facilities’ point cloud datasets

2. Instance Segmentation of Industrial Point Cloud Data

3. Scene understanding and model generation

4. Cyber-physical systems integration of building information models and the physical construction

5. Ali A. K. O. J. Lee and C. Park. 2020. “Near real-time monitoring of construction progress: Integration of extended reality and kinect V2.” In Proc. Int. Symp. on Automation and Robotics in Construction 24–31. Kitakyushu Japan: IAARC Publications. https://doi.org/10.22260/ISARC2020/0005.

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