“FactoryDecoder: Expertise-Free Digital Twin Generation and Modification Tool” by Du, Zhong, Zhou and Li
Conference:
Type(s):
Title:
- FactoryDecoder: Expertise-Free Digital Twin Generation and Modification Tool
Session/Category Title: Interactive Techniques
Presenter(s)/Author(s):
Abstract:
FactoryDecoder is a tool for non-expert users in 3D engineering to generate and modify digital twins. It translates descriptions of production lines into hierarchical asset codes that guides organizing layouts and device representations generating and positioning. It significantly reduces scenario building and 3D modeling costs with generative artificial intelligence.
References:
[1]
Agniva Banerjee, Raka Dalal, Sudip Mittal, and Karuna Pande Joshi. 2017. Generating Digital Twin Models using Knowledge Graphs for Industrial Production Lines. In Proceedings of the 2017 ACM on Web Science Conference, June 25, 2017. ACM, Troy New York USA, 425?430. https://doi.org/10.1145/3091478.3162383
[2]
Heewoo Jun and Alex Nichol. 2023. Shap-E: Generating Conditional 3D Implicit Functions. https://doi.org/10.48550/arXiv.2305.02463
[3]
Weiyu Li, Rui Chen, Xuelin Chen, and Ping Tan. 2023. SweetDreamer: Aligning Geometric Priors in 2D Diffusion for Consistent Text-to-3D. https://doi.org/10.48550/arXiv.2310.02596
[4]
Xiaoxiao Long, Yuan-Chen Guo, Cheng Lin, Yuan Liu, Zhiyang Dou, Lingjie Liu, Yuexin Ma, Song-Hai Zhang, Marc Habermann, Christian Theobalt, and Wenping Wang. 2023. Wonder3D: Single Image to 3D using Cross-Domain Diffusion. https://doi.org/10.48550/arXiv.2310.15008
[5]
Chunyi Sun, Junlin Han, Weijian Deng, Xinlong Wang, Zishan Qin, and Stephen Gould. 2023. 3D-GPT: Procedural 3D Modeling with Large Language Models. https://doi.org/10.48550/arXiv.2310.12945
