Large Language Model Empowered Spatio-Visual Queries for Extended Reality Environments
With the technological advances in creation and capture of 3D spatial data, new emerging applications are being developed. Digital Twins, metaverse and extended reality (XR) based immersive environments can be enriched by leveraging geocoded 3D spatial data. Unlike 2D spatial queries, queries involving 3D immersive environments need to take the query user’s viewpoint into account. Spatio-visual queries return objects that are visible from the user’s perspective.In this paper, we propose enhancing 3D spatio-visual queries with large language models (LLM). These kinds of queries allow a user to interact with the visible objects using a natural language interface. We have implemented a proof-of-concept prototype and conducted preliminary evaluation. Our results demonstrate the potential of truly interactive immersive environments.
Authors
- Suprio Ray (ORCID: https://orcid.org/0000-0003-0681-9685)
- Pulei Xiong (ORCID: https://orcid.org/0000-0002-3460-6946)
- Rongxing Lu (ORCID: https://orcid.org/0000-0001-5720-0941)
- Mohammadmasoud Shabanijou
- Vidit Sharma (ORCID: https://orcid.org/0000-0003-1287-1471)
Institutions
- University of New Brunswick (CA)
- National Research Council Canada (CA)
Publication Details
- Published
- 2024-12-15
- DOI
- https://doi.org/10.1109/bigdata62323.2024.10825084
- Citations
- 1
- Primary Topic
- Data Management and Algorithms
- Type
- article
- Field-Weighted Citation Impact
- 0.35
Funders
- National Research Council