Point Cloud–Based Concrete Surface Defect Semantic Segmentation-Reference-Cited by-同舟云学术

Point Cloud–Based Concrete Surface Defect Semantic Segmentation

Published:2023-03 Issue:2 Volume:37 Page:
ISSN:0887-3801
Container-title:Journal of Computing in Civil Engineering
language:en
Short-container-title:J. Comput. Civ. Eng.

Author:

Bolourian Neshat¹,Nasrollahi Majid²,Bahreini Fardin³^ORCID,Hammad Amin⁴^ORCID

Affiliation:

1. Research Assistant, Dept. of Building, Civil and Environmental Engineering, Concordia Univ., 1515 Sainte-Catherine St. West, Montreal, QC, Canada H3G 2W1.

2. Graduate Student, Artificial Intelligence and Risk Management Consulting Ltd., 1-1000 Lorimer Blvd., Winnipeg, MB, Canada R3P 1C8.

3. Ph.D. Candidate, Dept. of Building, Civil and Environmental Engineering, Concordia Univ., 1515 Sainte-Catherine St. West, Montreal, QC, Canada H3G 2W1. ORCID: .

4. Professor, Concordia Institute for Information Systems Engineering, Concordia Univ., 1515 Sainte-Catherine St. West, Montreal, QC, Canada H3G 2W1 (corresponding author). ORCID: .

Publisher

American Society of Civil Engineers (ASCE)

Subject

Computer Science Applications,Civil and Structural Engineering

Link

https://ascelibrary.org/doi/pdf/10.1061/JCCEE5.CPENG-5009

Reference68 articles.

1. Planning for terrestrial laser scanning in construction: A review

2. Bahreini F. and A. Hammad. 2021. “Point cloud semantic segmentation of concrete surface defects using dynamic graph CNN.” In Vol. 38 of Proc. 38th Int. Symp. on Automation and Robotics in Construction 379–386. Dubai United Arab Emirates: IAARC Publication.

3. Review: Deep Learning on 3D Point Clouds

4. Bolourian N. 2022. “Surface normal enhanced PointNet2.” Accessed May 12 2022. https://github.com/neshatbln/SNEPointNet2.

5. SnapNet: 3D point cloud semantic labeling with 2D deep segmentation networks

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