Segmentation of point clouds via joint semantic and geometric features for 3D modeling of the built environment-Reference-Cited by-同舟云学术

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Segmentation of point clouds via joint semantic and geometric features for 3D modeling of the built environment

Published:2021-05 Issue: Volume:125 Page:103584
ISSN:0926-5805
Container-title:Automation in Construction
language:en
Short-container-title:Automation in Construction

Author:

Perez-Perez Yeritza,Golparvar-Fard Mani,El-Rayes Khaled

Funder

National Science Foundation

Publisher

Elsevier BV

Subject

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

Reference69 articles.

1. Structural-based point cloud segmentation of highly ornate building façades for computational modelling;Hamid-Lakzaeian;Autom. Constr.,2019

2. 6d dbscan-based segmentation of building point clouds for planar object classification;Czerniawski;Autom. Constr.,2018

3. Voxel-based segmentation of 3d point clouds from construction sites using a probabilistic connectivity model;Xu;Pattern Recogn. Lett.,2018

4. Indoor 3d reconstruction from point clouds for optimal routing in complex buildings to support disaster management;Nikoohemat;Autom. Constr.,2020

5. An autonomous robotic platform for automatic extraction of detailed semantic models of buildings;Adán;Autom. Constr.,2020

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5. Enhancing Immersive Experiences through 3D Point Cloud Analysis: A Novel Framework for Applying 2D Visual Saliency Models to 3D Point Clouds;2024 16th International Conference on Quality of Multimedia Experience (QoMEX);2024-06-18

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