ICLR 2026 Oral
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
- Status
- Accepted as an Oral at ICLR 2026
- Authors
- Maksim Kolodiazhnyi, Denis Tarasov, Dmitrii Zhemchuzhnikov, Alexander Nikulin, Ilya Zisman, Anna Vorontsova, Anton Konushin, Vladislav Kurenkov, Danila Rukhovich
- Venue
- ICLR 2026 Oral
- Research area
- CAD Intelligence
- First arXiv submission
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 |
Values are attributed to the associated paper and the stated input and evaluation protocol.
Technologies and methods
- PyTorch
- PyTorch3D
- Transformers
- Qwen2-VL
- CadQuery
- Online reinforcement learning
Related M:3L research
Evidence boundary
Benchmark claims and numerical values are paper-reported results for the named protocol. Potential applications are not evidence of completed deployments.