Construction Scene Segmentation Using 3D Point Clouds: A Dataset and Challenges-Reference-Cited by-同舟云学术

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Construction Scene Segmentation Using 3D Point Clouds: A Dataset and Challenges

Published:2024-03-18 Issue: Volume:107 Page:378-385
ISSN:
Container-title:Construction Research Congress 2024
language:
Short-container-title:

Author:

Kim Seongyong¹²³,Kim Yeseul¹²³,Cho Yong K.¹²³

Affiliation:

1. Ph.D. Student, School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA. ORCID: .

2. Ph.D. Student, School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA.

3. Professor, School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA.

Publisher

American Society of Civil Engineers

Link

https://ascelibrary.org/doi/pdf/10.1061/9780784485262.039

Reference20 articles.

1. Armeni I. Sener O. Zamir A. R. Jiang H. Brilakis I. Fischer M. and Savarese S. (2016). 3d semantic parsing of large-scale indoor spaces. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 1534–1543.

2. Behley J. Garbade M. Milioto A. Quenzel J. Behnke S. Stachniss C. and Gall J. (2019). Semantickitti: A dataset for semantic scene understanding of lidar sequences. Proceedings of the IEEE/CVF International Conference on Computer Vision 9297–9307.

3. The value of integrating Scan-to-BIM and Scan-vs-BIM techniques for construction monitoring using laser scanning and BIM: The case of cylindrical MEP components

4. Tracking the Built Status of MEP Works: Assessing the Value of a Scan-vs-BIM System

5. Dai A. Chang A. X. Savva M. Halber M. Funkhouser T. and Nießner M. (2017). Scannet: Richly-annotated 3d reconstructions of indoor scenes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 5828–5839.

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