From Scans to Parametric BIM: An Enhanced Framework Using Synthetic Data Augmentation and Parametric Modeling for Highway Bridges-Reference-Cited by-同舟云学术

From Scans to Parametric BIM: An Enhanced Framework Using Synthetic Data Augmentation and Parametric Modeling for Highway Bridges

Published:2024-05 Issue:3 Volume:38 Page:
ISSN:0887-3801
Container-title:Journal of Computing in Civil Engineering
language:en
Short-container-title:J. Comput. Civ. Eng.

Author:

Yang Liu¹,Lin Yi-Chun²^ORCID,Cai Hubo³^ORCID,Habib Ayman⁴

Affiliation:

1. Division of Construction Engineering and Management, Lyles School of Civil Engineering, Purdue Univ., 550 Stadium Mall Dr., West Lafayette, IN 47907.

2. Division of Geomatics Engineering, Lyles School of Civil Engineering, Purdue Univ., 550 Stadium Mall Dr., West Lafayette, IN 47907. ORCID: .

3. Professor, Division of Construction Engineering and Management, Lyles School of Civil Engineering, Purdue Univ., 550 Stadium Mall Dr., West Lafayette, IN 47907 (corresponding author). ORCID: .

4. Professor, Division of Geomatics Engineering, Lyles School of Civil Engineering, Purdue Univ., 550 Stadium Mall Dr., West Lafayette, IN 47907.

Publisher

American Society of Civil Engineers (ASCE)

Link

https://ascelibrary.org/doi/pdf/10.1061/JCCEE5.CPENG-5640

Reference62 articles.

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3. 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 Conf. on Computer Vision and Pattern Recognition (CVPR) 1534–1543. New York: IEEE.

4. SCAN-TO-BIM METHODOLOGY ADAPTED FOR DIFFERENT APPLICATION

5. Barath D. and J. Matas. 2019. “Progressive-x: Efficient anytime multi-model fitting algorithm.” In Proc. IEEE/CVF Int. Conf. on Computer Vision 3780–3788. New York: IEEE.

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