Encoding 3D Point Contexts for Self-Supervised Spall Classification Using 3D Bridge Point Clouds-Reference-Cited by-同舟云学术

Encoding 3D Point Contexts for Self-Supervised Spall Classification Using 3D Bridge Point Clouds

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:

Kasireddy Varun¹^ORCID,Akinci Burcu²

Affiliation:

1. Ph.D. Candidate, Dept. of Civil and Environmental Engineering, Carnegie Mellon Univ., 5000 Forbes Ave., Pittsburgh, PA 15213 (corresponding author). ORCID: .

2. Paul P. Christiano Professor, Dept. of Civil and Environmental Engineering, Carnegie Mellon Univ., 5000 Forbes Ave., Pittsburgh, PA 15213.

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-5041

Reference73 articles.

1. AASHTO. 2013. Manual for bridge element inspection. Washington, DC: AASHTO.

2. Aljalbout E. V. Golkov Y. Siddiqui M. Strobel and D. Cremers. 2018. “Clustering with deep learning: Taxonomy and new methods.” Preprint submitted January 23 2018. https://arxiv.org/abs/1801.07648.

3. A novel unsupervised learning method for intelligent fault diagnosis of rolling element bearings based on deep functional auto-encoder

4. Armeni I. O. Sener A. R. Zamir H. Jiang I. Brilakis M. Fischer and S. Savarese. 2016. “3D semantic parsing of large-scale indoor spaces.” In Proc. IEEE Computation Social Conf. Computer Vision Pattern Recognition 1534–1543. New York: IEEE.

5. Deep learning-based automatic volumetric damage quantification using depth camera

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