Repository logo
  • Communities & Collections
  • All of DSpace
  • English
  • Čeština
  • Deutsch
  • Español
  • Français
  • Gàidhlig
  • Latviešu
  • Magyar
  • Nederlands
  • Português
  • Português do Brasil
  • Suomi
  • Svenska
  • Türkçe
  • Қазақ
  • বাংলা
  • हिंदी
  • Ελληνικά
  • Log In
    or
    New user? Click here to register.Have you forgotten your password?
  1. Home
  2. Browse by Author

Browsing by Author "Muralikrishna, Nikhil"

Now showing 1 - 1 of 1
Results Per Page
Sort Options
  • Loading...
    Thumbnail Image
    Item
    Prompting Meaning: Optimizing Prompt Engineering for Architectural Point Cloud Interpretation
    (The Eurographics Association, 2025) Paolanti, Marina; Muralikrishna, Nikhil; Gorgoglione, Lucrezia; Pierdicca, Roberto; Campana, Stefano; Ferdani, Daniele; Graf, Holger; Guidi, Gabriele; Hegarty, Zackary; Pescarin, Sofia; Remondino, Fabio
    Three-dimensional point cloud visualisation is essential for preserving and analysing built heritage by providing detailed insights into architectural forms and spatial configurations. Although human perception naturally integrates visual, spatial and contextual information, AI systems have yet to match this interpretive ability, particularly about 3D point clouds. This gap in interpretation highlights the need for AI approaches that process 3D data not only geometrically but also semantically. To address this challenge, the 3D.LLM project is exploring how combining point clouds with large language models (LLMs) can improve spatial and linguistic understanding. This paper presents a prompt engineering strategy developed as part of the 3D.LLM project to improve the semantic interpretation of architectural point clouds. By linking spatial attributes to language-based reasoning, LLMs are employed to generate richer and more accurate descriptions of cultural heritage environments. Unlike conventional geometric segmentation approaches, which often fail to capture architectural nuances, this system enables a spatially aware and flexible interpretation of 3D data. To refine the AI outputs and ensure spatial precision, domain-specific benchmarks such as ArCH and Objaverse XL have been employed. Preliminary findings suggest that prompt engineering significantly improves interpretability, descriptive accuracy and contextual depth, outperforming traditional automated methods. Beyond improving accessibility to architectural heritage information, this approach encourages interdisciplinary collaboration by making complex 3D structures more accessible and useful to scholars, conservators and a wider audience.

Eurographics Association © 2013-2025  |  System hosted at Graz University of Technology      
DSpace software copyright © 2002-2025 LYRASIS

  • Cookie settings
  • Privacy policy
  • End User Agreement
  • Send Feedback