IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/383270.html
   My bibliography  Save this article

Automatic 3D City Modeling Using a Digital Map and Panoramic Images from a Mobile Mapping System

Author

Listed:
  • Hyungki Kim
  • Yuna Kang
  • Soonhung Han

Abstract

Three-dimensional city models are becoming a valuable resource because of their close geospatial, geometrical, and visual relationship with the physical world. However, ground-oriented applications in virtual reality, 3D navigation, and civil engineering require a novel modeling approach, because the existing large-scale 3D city modeling methods do not provide rich visual information at ground level. This paper proposes a new framework for generating 3D city models that satisfy both the visual and the physical requirements for ground-oriented virtual reality applications. To ensure its usability, the framework must be cost-effective and allow for automated creation. To achieve these goals, we leverage a mobile mapping system that automatically gathers high-resolution images and supplements sensor information such as the position and direction of the captured images. To resolve problems stemming from sensor noise and occlusions, we develop a fusion technique to incorporate digital map data. This paper describes the major processes of the overall framework and the proposed techniques for each step and presents experimental results from a comparison with an existing 3D city model.

Suggested Citation

  • Hyungki Kim & Yuna Kang & Soonhung Han, 2014. "Automatic 3D City Modeling Using a Digital Map and Panoramic Images from a Mobile Mapping System," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-10, August.
  • Handle: RePEc:hin:jnlmpe:383270
    DOI: 10.1155/2014/383270
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2014/383270.pdf
    Download Restriction: no

    File URL: http://downloads.hindawi.com/journals/MPE/2014/383270.xml
    Download Restriction: no

    File URL: https://libkey.io/10.1155/2014/383270?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kerun Li, 2024. "Research on the Factors Influencing the Spatial Quality of High-Density Urban Streets: A Framework Using Deep Learning, Street Scene Images, and Principal Component Analysis," Land, MDPI, vol. 13(8), pages 1-26, July.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hin:jnlmpe:383270. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.