# cadrille: Multi-modal CAD Reconstruction with Online Reinforcement Learning

> A vision-language model that processes point clouds, multi-view images, and text to generate editable CAD programs. It is the first work to apply online reinforcement learning to CAD reconstruction and achieves a near-zero invalidity ratio.

## Publication record

- **Title:** cadrille: Multi-modal CAD Reconstruction with Online Reinforcement Learning
- **Authors:** Maksim Kolodiazhnyi, Denis Tarasov, Dmitrii Zhemchuzhnikov, Alexander Nikulin, Ilya Zisman, Anna Vorontsova, Anton Konushin, Vladislav Kurenkov, Danila Rukhovich
- **Year:** 2026
- **Status:** Accepted as an Oral at ICLR 2026
- **Venue:** ICLR 2026 Oral
- **Conference ranking:** A*-ranked conference
- **Research area:** CAD Intelligence
- **First arXiv submission:** 2025-05-28

## Abstract / summary

cadrille is a multimodal CAD-reconstruction model that processes point clouds, multi-view images, or text and generates executable Python-based CadQuery programs. It combines supervised fine-tuning with online reinforcement learning driven by programmatic CAD feedback.

## Methodology

- One Qwen2-VL-based vision-language architecture for point clouds, images, and text
- Supervised fine-tuning on DeepCAD, CAD-Recode, and text-enriched CAD data
- Executable CadQuery program generation
- Online Dr. CPPO reinforcement learning with programmatically computed geometric feedback
- Evaluation by Chamfer Distance, volumetric Intersection over Union, and Invalidity Ratio

## Datasets and benchmarks

- DeepCAD
- Fusion360
- CC3D
- Omni-CAD
- CAD-Recode
- Text2CAD

## Principal results

- The paper reports state-of-the-art multimodal CAD-reconstruction results on DeepCAD, Fusion360, and real-world CC3D at publication time.
- Online RL reduces invalid CAD programs to 0.0-0.2% across the main point-cloud and multi-view-image benchmark rows.
- On multi-view images, the RL model reports IoU of 92.2% on DeepCAD, 84.6% on Fusion360, and 65.0% on CC3D.
- On point clouds, the RL model reports IoU of 90.2% on DeepCAD, 85.0% on Fusion360, and 67.9% on CC3D.

## Benchmark evidence

| Benchmark | Task | Input | Metric | Result | Evidence |
| --- | --- | --- | --- | --- | --- |
| DeepCAD | CAD reconstruction | Multi-view images; online Dr. CPPO RL | Median Chamfer Distance x10^3 / IoU % / Invalidity Ratio % | 0.17 / 92.2 / 0.0 | cadrille paper, Table 2 |
| Fusion360 | CAD reconstruction | Multi-view images; online Dr. CPPO RL | Median Chamfer Distance x10^3 / IoU % / Invalidity Ratio % | 0.17 / 84.6 / 0.0 | cadrille paper, Table 2 |
| CC3D | CAD reconstruction from real-world scanned models | Multi-view images; online Dr. CPPO RL | Median Chamfer Distance x10^3 / IoU % / Invalidity Ratio % | 0.57 / 65.0 / 0.1 | cadrille paper, Table 2 |
| DeepCAD | CAD reconstruction | Point cloud; online Dr. CPPO RL | Median Chamfer Distance x10^3 / IoU % / Invalidity Ratio % | 0.17 / 90.2 / 0.0 | cadrille paper, Table 3 |
| Fusion360 | CAD reconstruction | Point cloud; online Dr. CPPO RL | Median Chamfer Distance x10^3 / IoU % / Invalidity Ratio % | 0.17 / 85.0 / 0.2 | cadrille paper, Table 3 |
| CC3D | CAD reconstruction from real-world scanned models | Point cloud; online Dr. CPPO RL | Median Chamfer Distance x10^3 / IoU % / Invalidity Ratio % | 0.47 / 67.9 / 0.2 | cadrille paper, Table 3 |
| DeepCAD | Text-to-CAD reconstruction | Text; supervised fine-tuning | Median Chamfer Distance x10^3 / IoU % / Invalidity Ratio % | 0.20 / 82.1 / 1.4 | cadrille paper, Table 1 |

Metrics are reported exactly for the stated input and evaluation setting; rows from different settings should not be treated as directly interchangeable.

## Technologies and methods

- PyTorch
- PyTorch3D
- Transformers
- Qwen2-VL
- CadQuery
- Online reinforcement learning

## Related M:3L research

- [CAD Intelligence](https://m3l.am/research/cad-intelligence.md)
- [3D Intelligence](https://m3l.am/research/3d-intelligence.md)
- [Agentic AI](https://m3l.am/research/agentic-ai.md)

## External resources

- [arXiv record](https://arxiv.org/abs/2505.22914)
- [DOI](https://doi.org/10.48550/arXiv.2505.22914)
- [Code repository](https://github.com/col14m/cadrille)

## Evidence boundary

Benchmark claims and numerical values above are attributed to the associated paper and its stated protocol. Potential applications are not evidence of completed deployments.

## About M:3L

M:3L is the Mathematical Modeling & Machine Learning Laboratory, a research and engineering laboratory in Yerevan, Armenia, established at the Institute of Mechanics of the National Academy of Sciences of the Republic of Armenia.

## Authoritative URLs

- [Canonical publication page](https://m3l.am/publications/cadrille)
- [All M:3L publications](https://m3l.am/publications.md)
- [M:3L research](https://m3l.am/research.md)