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2023
Conference Paper
Title

Semantic Wireframe Detection

Abstract
We present a conceptually simple and effective algorithm to detect user specified wireframes in a given indoor room image. Previous deep learning-based methods can produce great line detection results, however, they contain many redundant information for some cases. Hence, our method integrates semantic segmentation algorithm to control which part of the imagine should be detected. Segmentation is the task of clustering parts of an image together which belong to the same object class. According to the class information the proposed algorithm can show the desired results e.g. wireframe between walls and ceilings. Our method can give texture information and prepare for the following reconstruction.
Author(s)
Zhou, Yiming
Fraunhofer-Institut für Zerstörungsfreie Prüfverfahren IZFP  
Osman, Ahmad  
Fraunhofer-Institut für Zerstörungsfreie Prüfverfahren IZFP  
Willms, Marc
Fraunhofer-Institut für Zerstörungsfreie Prüfverfahren IZFP  
Kunz, Albrecht
Hochschule für Technik und Wirtschaft des Saarlandes  
Philipp, Selina
OBG Hochbau GmbH & Co.KG, Ottweiler
Blatt, Janine
OBG Hochbau GmbH & Co.KG, Ottweiler
Eul, Simon
OBG Hochbau GmbH & Co.KG, Ottweiler
Mainwork
DGZfP DACH-Jahrestagung 2023. Online resource  
Conference
Deutsche Gesellschaft für Zerstörungsfreie Prüfung (DGZfP Jahrestagung) 2023  
Open Access
File(s)
Download (725.04 KB)
Rights
CC BY 4.0: Creative Commons Attribution
DOI
10.24406/publica-2179
Language
English
Fraunhofer-Institut für Zerstörungsfreie Prüfverfahren IZFP  
Keyword(s)
  • Wireframe

  • Detection

  • semantic

  • segmentation

  • algorithm

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