neuralCAD-Edit: An Expert Benchmark for Multimodal-Instructed 3D CAD Model Editing

arXiv cs.CV / 4/20/2026

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Key Points

  • The paper introduces neuralCAD-Edit, a new benchmark for editing 3D CAD models created from real expert inputs from CAD engineers.
  • Unlike prior text-conditioned approaches, the dataset captures realistic editing requests by recording professional designers who directly manipulate CAD models in CAD software while speaking, pointing, and drawing.
  • The study compares leading foundation models against human CAD experts and finds a substantial performance gap across both automatic metrics and human evaluations.
  • In human acceptance trials, even the best foundation model (GPT 5.2) performs about 53% lower than human experts, highlighting the difficulty of multimodal instructed 3D CAD editing.
  • The authors release code/data to provide a baseline for future development of 3D CAD editing methods and foundation models.

Abstract

We introduce neuralCAD-Edit, the first benchmark for editing 3D CAD models collected from expert CAD engineers. Instead of text conditioning as in prior works, we collect realistic CAD editing requests by capturing videos of professional designers, interacting directly with CAD models in CAD software, while talking, pointing and drawing. We recruited ten consenting designers to contribute to this contained study. We benchmark leading foundation models against human CAD experts carrying out edits, and find a large performance gap in both automatic metrics and human evaluations. Even the best foundation model (GPT 5.2) scores 53% lower (absolute) than CAD experts in human acceptance trials, demonstrating the challenge of neuralCAD-Edit. We hope neuralCAD-Edit will provide a solid foundation against which 3D CAD editing approaches and foundation models can be developed. Code/data: https://autodeskailab.github.io/neuralCAD-Edit