CADENA-Bench

A benchmark for CAD reverse engineering on real mechanical parts, drawn from MCB, TMCAD and CADNet, stripped of trivial and duplicate geometry, and grouped into six part families so that a method's weaknesses are visible rather than averaged away.

3396 parts. Reported per family, because aggregate scores hide exactly the failure modes that matter: every learned method we evaluate loses roughly half its GMS moving from sketch–extrude corpora to these parts, and the loss is concentrated in revolved and patterned geometry.

• Paper: CADENA: Stepwise CAD Reverse Engineering (arXiv:2608.00799)
• Code: https://github.com/zhemdi/cadena
• Model: https://huggingface.co/kulibinai/cadena

Families

Family	Parts
shafts_and_bushings	762
gears_and_bearings	749
housings_and_frames	359
levers_and_profiles	463
springs_and_fasteners	1046
tooling_and_gauges	17
Total	3396

tooling_and_gauges is small enough that its column should not be read as a ranking.

Fields

Column	Contents
mesh_file	Target mesh filename
image	The eight target views tiled into one image — six axis-aligned, two isometric
ref	Reference CAD program
ref_path	Source path of the reference program
prompt	Prompt used for stepwise inference
family	One of the six part families

Contamination

The 3504-part evaluation set contains 108 meshes byte-identical to a mesh in the reinforcement-learning training corpus. Those are removed here, so this benchmark and the RL training set do not intersect. The removal is reproducible: hash every mesh in both sets and drop exact content matches.

Evaluation

The paper scores predictions with GMS, which counts a predicted point as matching a target point only when it is both close enough and carries a consistent normal (‖p − q‖ ≤ τ and n_p · n_q ≥ cos α), so the score reflects whether the surface was reconstructed rather than whether volume was filled. A prediction that fails to build or is not watertight is invalid; invalid predictions count toward the invalid rate and are excluded from the means.

from datasets import load_dataset

ds = load_dataset("kulibinai/cadena-bench", split="train")
gears = ds.filter(lambda r: r["family"] == "gears_and_bearings")

Citation

@article{cadena2026,
  title         = {CADENA: Stepwise CAD Reverse Engineering},
  author        = {Kabisov, Soslan and Savrasov, Gennadiy and Elistratov, Maksim and
                   Rodriguez, Antonio and Ignatiev, Daniil and Gavrilov, Nikita and
                   Uzdenov, Rustam and Boyko, Alexey I. and Pasechnik, Igor and
                   Konushin, Anton and Kuznetsov, Andrey and Zhemchuzhnikov, Dmitrii},
  year          = {2026},
  eprint        = {2608.00799},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CV},
  url           = {https://arxiv.org/abs/2608.00799}
}