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From PC2BIM: Automatic Model generation from Indoor Point Cloud

Published: 09 September 2019 Publication History

Abstract

In this paper, we present a system to automatically generate BIMs1 model from indoor point cloud. In contrary to previous works, our approach is able to take as input a point cloud with the minimum of information namely the points of coordinates (x, y, z) and produce excellent results. We first detect major flat surfaces such a walls, floor, and ceiling which are the bedrocks of our structure. Then, we present a novel 2D matrix template representation of walls which ease the operations like room layout and openings detection in polynomial time. Finally, we generate the BIM model rich with spatial and semantic information about the physical structures. A series of experiments performed show the efficiency and the precision of our approach.

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Cited By

View all
  • (2024)A Specialized Pipeline for Efficient and Reliable 3D Semantic Model Reconstruction of Buildings from Indoor Point CloudsJournal of Imaging10.3390/jimaging1010026110:10(261)Online publication date: 19-Oct-2024
  • (2023)Automação da modelagem BIM a partir de nuvens de pontosPARC Pesquisa em Arquitetura e Construção10.20396/parc.v14i00.866901514(e023010)Online publication date: 19-May-2023
  • (2020)A Novel Indoor Structure Extraction Based on Dense Point CloudISPRS International Journal of Geo-Information10.3390/ijgi91106609:11(660)Online publication date: 2-Nov-2020

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Published In

cover image ACM Other conferences
ICDSC 2019: Proceedings of the 13th International Conference on Distributed Smart Cameras
September 2019
172 pages
ISBN:9781450371896
DOI:10.1145/3349801
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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  • University of Trento

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 September 2019

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Author Tags

  1. BIM
  2. Point Cloud
  3. RANSAC
  4. Undirected graph

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  • Research-article
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  • Refereed limited

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ICDSC 2019

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Overall Acceptance Rate 92 of 117 submissions, 79%

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Cited By

View all
  • (2024)A Specialized Pipeline for Efficient and Reliable 3D Semantic Model Reconstruction of Buildings from Indoor Point CloudsJournal of Imaging10.3390/jimaging1010026110:10(261)Online publication date: 19-Oct-2024
  • (2023)Automação da modelagem BIM a partir de nuvens de pontosPARC Pesquisa em Arquitetura e Construção10.20396/parc.v14i00.866901514(e023010)Online publication date: 19-May-2023
  • (2020)A Novel Indoor Structure Extraction Based on Dense Point CloudISPRS International Journal of Geo-Information10.3390/ijgi91106609:11(660)Online publication date: 2-Nov-2020

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