Instance Segmentation of Industrial Point Cloud Data-Reference-Cited by-同舟云学术

Instance Segmentation of Industrial Point Cloud Data

Published:2021-11 Issue:6 Volume:35 Page:04021022
ISSN:0887-3801
Container-title:Journal of Computing in Civil Engineering
language:en
Short-container-title:J. Comput. Civ. Eng.

Author:

Agapaki Eva¹^ORCID,Brilakis Ioannis²^ORCID

Affiliation:

1. Innovation Lead, PTC Inc., 121 Seaport Blvd., Boston, MA 02210 (corresponding author). ORCID: .

2. Laing O’Rourke Reader, Dept. of Engineering, Univ. of Cambridge, Cambridge CB2 1PZ, UK. ORCID:

Publisher

American Society of Civil Engineers (ASCE)

Subject

Computer Science Applications,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29CP.1943-5487.0000972

Reference65 articles.

1. Agapaki E. 2020. “Automated object segmentation in existing industrial facilities.” Ph.D. thesis Dept. of Engineering Univ. of Cambridge.

2. Agapaki E. and I. Brilakis. 2017. “Prioritising object types of industrial facilities to reduce as-is modelling time.” In Proc. 33rd Annual ARCOM Conf. 402–411. Glasgow UK: Association of Researchers in Construction Management.

3. State-of-Practice on As-Is Modelling of Industrial Facilities

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

5. Agapaki E. A. Glyn-Davies S. Mandoki and I. Brilakis. 2019. “CLOI: A shape classification benchmark dataset for industrial facilities.” In Proc. 2019 ASCE Int. Conf. on Computing in Civil Engineering. Reston VA: ASCE.

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