Publications

@inproceedings{lee_knowledge_2023,

title = {Knowledge Generation Pipeline using LLM for Building 3D Object Knowledge Base},

author = {S. Lee and H. Lee and K. Lee},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184593202&doi=10.1109%2fICTC58733.2023.10392933&partnerID=40&md5=b877638607a04e5a31a2d5723af6e11b},

doi = {10.1109/ICTC58733.2023.10392933},

isbn = {21621233 (ISSN); 979-835031327-7 (ISBN)},

year  = {2023},

date = {2023-01-01},

booktitle = {Int. Conf. ICT Convergence},

pages = {1303–1305},

publisher = {IEEE Computer Society},

abstract = {With the wide spread of XR(eXtended Reality) contents such as Metaverse and VR(Virtual Reality) / AR(Augmented Reality), the utilization and importance of 3D objects are increasing. In this paper, we describe a knowledge generation pipeline of 3D object for reuse of existing 3D objects and production of new 3D object using generative AI(Artificial Intelligence). 3D object knowledge includes not only the object itself data that are generated in object editing phase but the information for human to recognize and understand objects. The target 3D model for building knowledge is the space model of office for business Metaverse service and the model of objects composing the space. LLM(Large Language Model)-based multimodal AI was used to extract knowledge from 3D model in a systematic and automated way. We plan to expand the pipeline to utilize knowledge base for managing extracted knowledge and correcting errors occurred during the LLM process for the knowledge extraction. © 2023 IEEE.},

keywords = {3D modeling, 3D models, 3D object, 3d-modeling, Augmented Reality, Data Mining, Knowledge Base, Knowledge based systems, Knowledge generations, Language Model, Metaverse, Metaverses, Multi-modal, MultiModal AI, Multimodal artificial intelligence, Pipelines, Virtual Reality, XR},

pubstate = {published},

tppubtype = {inproceedings}

}