CADReasoner: Iterative Program Editing for CAD Reverse Engineering
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866911558064406528 |
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| author | Kabisov, Soslan Kirichuk, Vsevolod Volkov, Andrey Savrasov, Gennadii Barannikov, Marina Konushin, Anton Kuznetsov, Andrey Zhemchuzhnikov, Dmitrii |
| author_facet | Kabisov, Soslan Kirichuk, Vsevolod Volkov, Andrey Savrasov, Gennadii Barannikov, Marina Konushin, Anton Kuznetsov, Andrey Zhemchuzhnikov, Dmitrii |
| contents | Computer-Aided Design (CAD) powers modern engineering, yet producing high-quality parts still demands substantial expert effort. Many AI systems tackle CAD reverse engineering, but most are single-pass and miss fine geometric details. In contrast, human engineers compare the input shape with the reconstruction and iteratively modify the design based on remaining discrepancies. Agent-based methods mimic this loop with frozen VLMs, but weak 3D grounding of current foundation models limits reliability and efficiency. We introduce CADReasoner, a model trained to iteratively refine its prediction using geometric discrepancy between the input and the predicted shape. The model outputs a runnable CadQuery Python program whose rendered mesh is fed back at the next step. CADReasoner fuses multi-view renders and point clouds as complementary modalities. To bridge the realism gap, we propose a scan-simulation protocol applied during both training and evaluation. Across DeepCAD, Fusion 360, and MCB benchmarks, CADReasoner attains state-of-the-art results on clean and scan-sim tracks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_29847 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CADReasoner: Iterative Program Editing for CAD Reverse Engineering Kabisov, Soslan Kirichuk, Vsevolod Volkov, Andrey Savrasov, Gennadii Barannikov, Marina Konushin, Anton Kuznetsov, Andrey Zhemchuzhnikov, Dmitrii Graphics Computer Vision and Pattern Recognition Human-Computer Interaction Computer-Aided Design (CAD) powers modern engineering, yet producing high-quality parts still demands substantial expert effort. Many AI systems tackle CAD reverse engineering, but most are single-pass and miss fine geometric details. In contrast, human engineers compare the input shape with the reconstruction and iteratively modify the design based on remaining discrepancies. Agent-based methods mimic this loop with frozen VLMs, but weak 3D grounding of current foundation models limits reliability and efficiency. We introduce CADReasoner, a model trained to iteratively refine its prediction using geometric discrepancy between the input and the predicted shape. The model outputs a runnable CadQuery Python program whose rendered mesh is fed back at the next step. CADReasoner fuses multi-view renders and point clouds as complementary modalities. To bridge the realism gap, we propose a scan-simulation protocol applied during both training and evaluation. Across DeepCAD, Fusion 360, and MCB benchmarks, CADReasoner attains state-of-the-art results on clean and scan-sim tracks. |
| title | CADReasoner: Iterative Program Editing for CAD Reverse Engineering |
| topic | Graphics Computer Vision and Pattern Recognition Human-Computer Interaction |
| url | https://arxiv.org/abs/2603.29847 |