aiqbench analysis
This research addresses a critical gap in generative design by introducing ExpConCAD, a framework that infers missing spatial constraints from natural language descriptions using learned construction structures and reusable design experiences. By generating executable CadQuery programs directly from underspecified inputs, the method advances the practical utility of text-to-CAD systems for real-world engineering workflows. The study highlights how experience memory and structural understanding are essential for completing implicit geometric requirements in automated design generation.
ExpConCAD: Experience-Guided Text-to-CAD Generation from Shape Descriptions with Implicit Spatial Constraints
arXiv:2608.24760v1 Announce Type: new Abstract: 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. Our code is available at: https://github.com/Hotjiashell/ExpConCAD.
Abstract: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. Our code is available at: this https URL.
Computation and Language (cs.CL)
arXiv:2608.24760 [cs.CL]
(or arXiv:2608.24760v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2608.24760 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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