IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-031-71287-6_3.html
   My bibliography  Save this book chapter

New Frontiers of Image-Based Surveying

In: Machine Learning and Mixed Reality for the Enhancement of Cultural Heritage

Author

Listed:
  • Maurizio Perticarini

    (University of Padua, DICEA)

Abstract

The development of Nvidia RTX GPUs and neural networks, such as Neural Radiance Field (NeRF), has revolutionized 3D graphics. These technologies enhance realism and rendering efficiency. NeRF generates 3D views from 2D images, reducing computation times and replacing GAN networks. Nvidia’s Instant NeRF algorithm uses multi-resolution hash grid encoding to speed up rendering, allowing detailed scenes to be created from a few photos. Applications include the reconstruction of moving objects and the surveying of inaccessible artworks, opening new frontiers in spatial representation.

Suggested Citation

  • Maurizio Perticarini, 2024. "New Frontiers of Image-Based Surveying," Springer Books, in: Machine Learning and Mixed Reality for the Enhancement of Cultural Heritage, chapter 0, pages 53-59, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-71287-6_3
    DOI: 10.1007/978-3-031-71287-6_3
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    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:spr:sprchp:978-3-031-71287-6_3. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.