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Steininger, S., Zhao, J., & Fottner, J. (2025).
“Enhancing computer‑aided design with deep learning frameworks: a literature review.” Proceedings of the Design Society (ICED 2025).
SurveyDeep LearningCAD
Maps ML/GenAI methods entering CAD and highlights automation gaps in drafting.
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Wang, H., et al. (2024).
“VQ‑CAD: Computer‑Aided Design model generation with vector‑quantized diffusion.” Computer‑Aided Design.
DiffusionParametric CAD
Shows how diffusion models can generate parametric CAD sequences—future auto‑drafting backbone.
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Zhou, S., et al. (2024).
“CADGen: Computer‑aided design sequence construction with deep learning.” IET Digital Graphic Technology.
Sequence LearningFeature History
Learns sketch/extrude histories that mirror real drafting/modeling steps.
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Li, P., et al. (2025).
“Revisiting CAD Model Generation by Learning Raster Sketch (RECAD).” AAAI Conference on Artificial Intelligence.
Sketch → CADRaster Inputs
Converts raster drawings into CAD solids; supports future plan‑to‑model automation.
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Fan, R., et al. (2025).
“A history‑based parametric CAD sketch dataset …” Computer‑Aided Design.
DatasetParametric Sketching
Provides training data for AI systems that understand drafting intent.
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Betsas, T., et al. (2024).
“INCAD: 2D vector drawings creation using instance segmentation.” ISPRS Archives (peer‑reviewed proceedings).
2D VectorizationSegmentation
Instance‑segmentation approach to produce clean vectors from drawings.
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Egiazarian, V., et al. (2020).
“Deep Vectorization of Technical Drawings.” ECCV 2020 Proceedings.
Line CleanupLegacy Plans
Foundational deep‑learning method for extracting crisp vectors from noisy scans.
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Mafipour, M. S., et al. (2023).
“Digitalization of 2D Bridge Drawings Using Deep Learning Models …” Peer‑reviewed engineering venue.
Legacy CAD2D → Parametric
A real pipeline for converting old technical sheets into editable CAD.
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Villena‑Toro, J., et al. (2022).
“Automated and Customized CAD Drawings by Utilizing …” ASME IDETC‑CIE Proceedings.
AutomationCustomization
Shows ML‑assisted automation inside CAD drafting deliverables.
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Daareyni, A., et al. (2025).
“Generative AI meets CAD: enhancing engineering design …” International Journal of Advanced Manufacturing Technology.
LLM CopilotText‑to‑CAD
Natural‑language CAD editing framework—preview of conversational drafting.
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Li, K. Y., et al. (2025).
“Generative AI and CAD automation for diverse and novel design …” SN Applied Sciences.
Generative CADAutomation
Combines GenAI with CAD automation—useful for forecasting drafting throughput changes.
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Li, Q., et al. (2025).
“Intelligent Generation and Reinforcement Learning …” Computer‑Aided Design (Special Issue).
Reinforcement LearningCAD Histories
RL agents learn drafting/modeling policies to produce valid CAD sequences.
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Lin, R., et al. (2025).
“A Survey on Deep Learning in 3D CAD Reconstruction.” Applied Sciences.
Survey3D Reconstruction
Comprehensive survey of reconstruction methods that feed auto‑drafting from images/scans.
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Peckham, O., et al. (2025).
“Artificial Intelligence in Generative Design: A Structured Review.” Designs.
SurveyGenerative Design
Structured review with strong implications for CAD automation and drafting roles.