ArXiv

Text-to-CAD Evaluation with CADTests

2026-05-08 · 14:41 UTC ·Dimitrios Mallis, Marco Wang, Ahmet Serdar Karadeniz... ·1 min read
Authors
Dimitrios Mallis, Marco Wang, Ahmet Serdar Karadeniz...
Categories
cs.CV, cs.AI, cs.LG, cs.RO
arXiv
https://arxiv.org/abs/2605.07807v1
PDF
https://arxiv.org/pdf/2605.07807v1

Brief

Text-to-CAD evaluation is framed as automated testing in this work: Mallis et al. propose CADTestBench and CADTests, executable checks that validate geometric and topological constraints of generated CAD models. They benchmark recent Text-to-CAD systems on CADTestBench and demonstrate that using CADTests to guide generation yields simple baselines that outperform prior methods; code and datasets are open-sourced.

Why it matters

Mallis, Wang, Karadeniz, Ricci, Kacem, and Aouada (arXiv 2026-05-08) introduce CADTestBench and CADTests: the first test-based benchmark for Text-to-CAD where CADTests are executable software tests that verify geometric and topological requirements of generated CAD models.

Key details

  • Using CADTestBench the authors benchmark recent Text-to-CAD methods and show CADTests can also guide model generation, producing simple baselines that surpass current methods; code and data are published on GitHub and as a Hugging Face dataset.
Source evidence

Abstract

Text-to-CAD has recently emerged as an important task with the potential to substantially accelerate design workflows. Despite its significance, there has been surprisingly little work on Text-to-CAD evaluation, and assessing CAD model generation performance remains a considerable challenge. In this work, we introduce a new evaluation perspective for Text-to-CAD based on automated testing. We propose CADTestBench, the first test-based benchmark for Text-to-CAD, based on CADTests, executable software tests that verify whether a generated CAD model satisfies the geometric and topological requirements of the input prompt. Using CADTestBench, we conduct comprehensive benchmarking of recent Text-to-CAD methods and further demonstrate that CADTests can also guide CAD model generation, yielding simple baselines that surpass performance of current methods. CADTestBench code and data are available at GitHub and Hugging Face dataset.