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EXPConCAD: Experience-Guided Text-to-CAD Generation from Shape Descriptions with Implicit Spatial Constraints

Jingyao Liu, Jinkang Tang

2024encaddesigntext-to-cadmachine learningspatial constraints

Abstract

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Text-to-CAD aims to generate executable CAD programs from natural-language descriptions. However, real-world descriptions are often underspecified and omit critical spatial constraints required for valid CAD construction, a challenge that has been largely overlooked by existing methods. In this paper, we argue that missing spatial constraints should be inferred with respect to the underlying construction structure and informed by reusable design experience. Based on this insight, we propose ExpConCAD, an experience-enhanced framework for implicit spatial constraint completion. ExpConCAD first recovers the intended construction structure and constraint scopes, then retrieves relevant constraint-completion experience for similar scopes to complete the missing spatial constraints, and finally generates executable CadQuery programs. Extensive experiments demonstrate the effectiveness of ExpConCAD and provide insights into the role of construction structure understanding and experience memory in spatial constraint completion.

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Cite This Work

@article{122e67fa-2622-4a1f-8e82-3f0e38719434,
  title={EXPConCAD: Experience-Guided Text-to-CAD Generation from Shape Descriptions with Implicit Spatial Constraints},
  author={Jingyao Liu and Jinkang Tang},
  year={2024},
  language={en}
}
TY  - JOUR
TI  - EXPConCAD: Experience-Guided Text-to-CAD Generation from Shape Descriptions with Implicit Spatial Constraints
AU  - Jingyao Liu
AU  - Jinkang Tang
PY  - 2024
LA  - en
ER  -

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