MIRAGE-CAD: Construction-Mediated Multimodal Generation of Executable CAD Programs
arXiv:2608.28669v1 Announce Type: new Abstract: Recovering an executable parametric CAD program from an observed object is fundamentally ambiguous, because the same final geometry can result from different construction procedures. We study this problem from four types of input: natural-language descriptions, rendered images, point clouds, and STEP/B-Rep geometry. MIRAGE-CAD maps each input to a shared construction representation and mediates program generation through an explicit construction-plan interface. The resulting Python CAD code is executed by an OpenCASCADE kernel to build the solid and export it as STEP. On 2,500 held-out queries per modality, the system achieves 55.4-70.0% build success and 52.3-66.2% STEP export success without retrieval at inference. Controlled comparisons show that strong reconstruction does not depend on expressing the construction representation as text: a decoder conditioned directly on the continuous representation also reconstructs strongly, while an exposure-matched plan-based decoder shows no detected material loss in per-part geometric fidelity. The explicit plan instead provides a readable and separately measurable intermediate representation whose agreement with the reference construction is informative about downstream execution success. Finally, we show that executable validity, geometric fidelity, and parametric responsiveness can diverge substantially and should therefore be evaluated separately.
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[Submitted on 24 Aug 2026]
Title:MIRAGE-CAD: Construction-Mediated Multimodal Generation of Executable CAD Programs
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Abstract:Recovering an executable parametric CAD program from an observed object is fundamentally ambiguous, because the same final geometry can result from different construction procedures. We study this problem from four types of input: natural-language descriptions, rendered images, point clouds, and STEP/B-Rep geometry. MIRAGE-CAD maps each input to a shared construction representation and mediates program generation through an explicit construction-plan interface. The resulting Python CAD code is executed by an OpenCASCADE kernel to build the solid and export it as STEP. On 2,500 held-out queries per modality, the system achieves 55.4-70.0% build success and 52.3-66.2% STEP export success without retrieval at inference. Controlled comparisons show that strong reconstruction does not depend on expressing the construction representation as text: a decoder conditioned directly on the continuous representation also reconstructs strongly, while an exposure-matched plan-based decoder shows no detected material loss in per-part geometric fidelity. The explicit plan instead provides a readable and separately measurable intermediate representation whose agreement with the reference construction is informative about downstream execution success. Finally, we show that executable validity, geometric fidelity, and parametric responsiveness can diverge substantially and should therefore be evaluated separately.
Comments: 64 pages, 5 figures, 20 tables, 7 appendices. Code, evaluation scripts and run reports: this https URL
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Programming Languages (cs.PL); Software Engineering (cs.SE)
ACM classes: I.3.5; I.2.6; I.2.7
Cite as: arXiv:2608.28669 [cs.CV]
(or arXiv:2608.28669v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2608.28669
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Jizong Zhan [view email] [v1] Mon, 24 Aug 2026 09:45:36 UTC (1,040 KB)
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