Text2CAD-Bench supplies 600 dual-prompt examples across four geometric and domain levels to test LLMs on text-to-parametric CAD, finding solid basic performance but sharp drops on complex topology and advanced features.
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HistCAD provides a constraint-aware parametric CAD representation, a dataset of 170k industrial sequences, and an editability benchmark with metrics ER, cPCSR, and OES to evaluate preservation of design intent.
A generative model writes programs in a relational constraint DSL and uses bootstrapping to learn object placement distributions that align more closely with human annotations than data-driven or LLM baselines.
An extrusion segmentation strategy decomposes CAD models into partial shapes to increase data diversity and improve deep learning reconstruction from point clouds.
citing papers explorer
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Text2CAD-Bench: A Benchmark for LLM-based Text-to-Parametric CAD Generation
Text2CAD-Bench supplies 600 dual-prompt examples across four geometric and domain levels to test LLMs on text-to-parametric CAD, finding solid basic performance but sharp drops on complex topology and advanced features.
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HistCAD: A Constraint-Aware Parametric History-Based CAD Representation, Dataset, and Benchmark with Industrial Complexity
HistCAD provides a constraint-aware parametric CAD representation, a dataset of 170k industrial sequences, and an editability benchmark with metrics ER, cPCSR, and OES to evaluate preservation of design intent.
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Learning to Place Objects with Programs and Iterative Self Training
A generative model writes programs in a relational constraint DSL and uses bootstrapping to learn object placement distributions that align more closely with human annotations than data-driven or LLM baselines.
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Extrusion Segmentation Strategy to improve CAD Reconstruction from Point Cloud
An extrusion segmentation strategy decomposes CAD models into partial shapes to increase data diversity and improve deep learning reconstruction from point clouds.