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Improved Edge Folding Algorithm for 3D Building Models Taking into Account the Visual Features

Auteur(s):




Médium: article de revue
Langue(s): anglais
Publié dans: Buildings, , n. 11, v. 13
Page(s): 2739
DOI: 10.3390/buildings13112739
Abstrait:

Simplifying 3D building models, effectively reducing model complexity and improving mapping efficiency, is an important part of 3D GIS. In order to address the problem that the simplification basis considered by most 3D building model data compression algorithms is mainly based on geometric features and fails to retain the visual features of the model, this paper proposed a half-folding simplification algorithm for 3D building models that took the visual features into account. The algorithm incorporated regional boundary constraints in the pre-processing stage to focus on preserving the boundary features of the model, and incorporated visual constraints, such as the vertex importance, edge length, and texture, in the calculation of the edge folding cost to construct a new error metric model. The simplification order was changed by redefining the edge folding cost, and a trade-off between compression rate and retention rate was made by using half-edge folding to maximise the retention of the visual features of the building. The experimental results showed that the algorithm could effectively reduce the number of elements drawn by the system and achieve a high level of detail information retention in the visual feature region of the model with a good visual representation.

Copyright: © 2023 by the authors; licensee MDPI, Basel, Switzerland.
License:

Cette oeuvre a été publiée sous la license Creative Commons Attribution 4.0 (CC-BY 4.0). Il est autorisé de partager et adapter l'oeuvre tant que l'auteur est crédité et la license est indiquée (avec le lien ci-dessus). Vous devez aussi indiquer si des changements on été fait vis-à-vis de l'original.

  • Informations
    sur cette fiche
  • Reference-ID
    10754140
  • Publié(e) le:
    14.01.2024
  • Modifié(e) le:
    07.02.2024
 
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