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.
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CADENA reconstructs a 3D mesh as an editable parametric CAD program. Instead of emitting the whole program in one pass, it adds one operation at a time, executes the partial program, and compares the target against what has been built so far — so the model always sees what is still missing, the way a human engineer works.
We also release CADENA-Bench: 3396 real mechanical parts from three industrial corpora, de-duplicated and grouped into six part families, reported per family so a method's weaknesses stay visible instead of averaging away. It is hard in a way existing test sets are not — every learned baseline loses roughly half its score moving from DeepCAD to real parts.
CADENA outperforms prior methods on DeepCAD, Fusion 360, MCB and CADENA-Bench, and reaches 0.910 voxel IoU on BenchCAD's Vision2Code. Reinforcement learning against executed geometry improves accuracy and cuts the invalid rate to 0.9%, since an operation that fails to build earns no reward.
Weights (SFT and RL), the benchmark, and inference code are all released.
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