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When LLMs step into the 3D world: a survey and meta-analysis of 3D tasks via multi-modal Large Language Models

Abstract:

As large language models (LLMs) evolve, their integration with 3D spatial data (3D-LLMs) has seen rapid progress, offering unprecedented capabilities for understanding and interacting with physical spaces. This survey provides a comprehensive overview of the methodologies enabling LLMs to process, understand, and generate 3D data. Highlighting the unique advantages of LLMs, such as in-context learning, step-by-step reasoning, open-vocabulary capabilities, and extensive world knowledge, we und...

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Publication status:
Accepted
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
Publisher:
IEEE
Acceptance date:
2024-02-26
Event title:
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2024)
Event location:
Seattle, WA, USA
Event website:
https://cvpr.thecvf.com/
Event start date:
2024-06-17
Event end date:
2024-06-21
Language:
English
Keywords:
Pubs id:
2013444
Local pid:
pubs:2013444
Deposit date:
2024-07-10

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