AUTOMATED BIM ENTITY RECONSTRUCTION FROM UNSTRUCTURED 3D POINTCLOUDS

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F21%3A00352058" target="_blank" >RIV/68407700:21110/21:00352058 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.14311/APP.2021.30.0126" target="_blank" >http://dx.doi.org/10.14311/APP.2021.30.0126</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.14311/APP.2021.30.0126" target="_blank" >10.14311/APP.2021.30.0126</a>

Jazyk výsledku

angličtina

Název v původním jazyce

AUTOMATED BIM ENTITY RECONSTRUCTION FROM UNSTRUCTURED 3D POINTCLOUDS

Popis výsledku v původním jazyce

Laser scanning is used widely in architecture and construction to document existing buildings by providing accurate data for creating a 3D model. The output is a set of data points in space, so-called point cloud. While point clouds can be directly rendered and inspected, they do not hold any semantics. Typically, engineers manually obtain floor plans, structural models, or the whole BIM model, which is a very time-consuming task for large building projects. In this contribution, we present the design and concept of a PointCloud2BIM library [1]. It provides a set of algorithms for automated or user assisted detection of fundamental entities from scanned point cloud data sets, such as floors, rooms, walls, and openings, and identification of the mutual relationships between them. The entity detection is based on a reasonable degree of human interaction (i.e., expected wall thickness). The results reside in a platform-agnostic JSON database allowing future integration into any existing BIM software.

Název v anglickém jazyce

AUTOMATED BIM ENTITY RECONSTRUCTION FROM UNSTRUCTURED 3D POINTCLOUDS

Popis výsledku anglicky

Laser scanning is used widely in architecture and construction to document existing buildings by providing accurate data for creating a 3D model. The output is a set of data points in space, so-called point cloud. While point clouds can be directly rendered and inspected, they do not hold any semantics. Typically, engineers manually obtain floor plans, structural models, or the whole BIM model, which is a very time-consuming task for large building projects. In this contribution, we present the design and concept of a PointCloud2BIM library [1]. It provides a set of algorithms for automated or user assisted detection of fundamental entities from scanned point cloud data sets, such as floors, rooms, walls, and openings, and identification of the mutual relationships between them. The entity detection is based on a reasonable degree of human interaction (i.e., expected wall thickness). The results reside in a platform-agnostic JSON database allowing future integration into any existing BIM software.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20101 - Civil engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

NMM 2020 Nano & Macro Mechanics

ISBN

978-80-01-06840-3

ISSN

—

e-ISSN

2336-5382

Počet stran výsledku

5

Strana od-do

126-130

Název nakladatele

České vysoké učení technické v Praze

Místo vydání

Praha

Místo konání akce

Praha

Datum konání akce

17. 9. 2020

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

—