This work introduces CADENA (Spanish for"chain"), a model that reconstructs a 3D mesh as a parametric CAD program, growing its sequence of operations one at a time and comparing the target with the currently predicted geometry at every step.
Abstract
Computer-Aided Design (CAD) underpins modern engineering, yet converting existing shapes into editable models still demands substantial expert effort. Most AI systems emit the entire CAD program in a single pass, never inspecting the intermediate geometry. In contrast, human engineers build a part feature by feature, checking after each operation what remains to be modeled. We introduce CADENA (Spanish for"chain"), a model that reconstructs a 3D mesh as a parametric CAD program, growing its sequence of operations one at a time and comparing the target with the currently predicted geometry at every step. We also address the lack of benchmarks for evaluating reverse-engineering methods on mechanical parts, introducing CADENA-Bench, a benchmark that measures performance across categories of mechanical parts. CADENA outperforms prior methods on CADENA-Bench and on the DeepCAD, Fusion 360, and MCB datasets. Code is available at https://github.com/zhemdi/cadena, model weights at https://huggingface.co/kulibinai/cadena, and CADENA-Bench at https://huggingface.co/datasets/kulibinai/cadena-bench.
Results show that execution alone is insufficient to characterize realistic CAD modeling and that frontier models and agents differ substantially across executability, IoUs, and visual-semantic identity.
Guan-Ling Li, Zhi-Chao Huang, Hui-Mu Yu et al.· 0 citations
The results show that CAD evaluation must test engineering behavior rather than appearance alone, while editing supplied CAD is substantially easier than generating it, while complex edits and matched FEA remain difficult.
This work presents a dataset of over 95,000 native OpenSCAD models, primarily containing mechanical parts and engineering components, designed specifically for the programmatic generation of 3D models, and aims to open new opportunities for programmatic 3D model generation.
D. Fresacher, Klaus Diepold· Proceedings of the 32nd ACM...· 0 citations
CANTO is introduced, a transformer neural operator that maps directly from continuous CAD geometry to physical fields, without meshing the input geometry, and achieves state-of-the-art accuracy on most evaluated surface and volume prediction tasks.
Daniel V. Leibovici, N. Kovachki, Dawon Ahn et al.· 0 citations
It is found that weaker agents often fail before producing a valid artifact, whereas stronger agents increasingly fail on structural, geometric, and construction-process requirements, and CADWorld exposes a gap between general GUI competence and reliable execution of persistent, verifiable engineering workflows.
Zi-Han Dong, Yuan-Zhe Liu, Zhi-Yuan Ma et al.· 1 citation
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