Automated semantic segmentation of industrial point clouds using ResPointNet++-Reference-Cited by-同舟云学术

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Automated semantic segmentation of industrial point clouds using ResPointNet++

Published:2021-10 Issue: Volume:130 Page:103874
ISSN:0926-5805
Container-title:Automation in Construction
language:en
Short-container-title:Automation in Construction

Author:

Yin Chao,Wang Boyu,Gan Vincent J.L.,Wang Mingzhu,Cheng Jack C.P.

Funder

Hong Kong University of Science and Technology

Innovation and Technology Commission

Publisher

Elsevier BV

Subject

Building and Construction,Civil and Structural Engineering,Control and Systems Engineering

Reference60 articles.

1. Building information modeling (BIM) for existing buildings – literature review and future needs;Volk;Autom. Constr.,2014

2. An application oriented Scan-to-BIM framework;Wang;Remote Sens.,2019

3. A review of the efforts and roles of the public sector for BIM adoption worldwide;Cheng;J. Inf. Technol. Constr.,2015

4. 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;Bosché;Autom. Constr.,2015

5. A framework for dimensional and surface quality assessment of precast concrete elements using BIM and 3D laser scanning;Kim;Autom. Constr.,2015

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2. Deep learning applications for point clouds in the construction industry;Automation in Construction;2024-12

3. Automated generation and semantic segmentation of roof orthophoto for digital twin -based monitoring of slated roofs;Automation in Construction;2024-11

4. Improved building MEP systems semantic segmentation in point clouds using a novel multi-class dataset and local–global vector transformer network;Journal of Building Engineering;2024-11

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