mlsanityData to 14 Oct 2026

VisCAD: A Foundation Model Suite with Multimodal Industrial CAD Intelligence

arXiv 2609.03811 ↗First posted 3 Sep 2026cs.CV cs.CLcited by 0

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Abstract (arXiv)

AI-assisted computer-aided design (CAD) for industrial products involves two challenging phases. Part-level generation maps diverse forms of user intent, including renders, text descriptions, 2D drawings, and real photographs, to executable programs in a CAD domain-specific language. Assembly-level generation must additionally handle interacting parts, plan mating relations, estimate poses, and place all parts correctly. Existing specialized CAD models are commonly trained on narrow input domains, such as renders or texts, and often generalize poorly, while general-purpose frontier models cover broader inputs but perform inconsistently across CAD domains. We present VisCAD, a foundation model suite designed to provide both broad generalization and strong CAD capability for realistic industrial products. At its core is VisCAD-M1, a 27B model trained through mid-training and post-training for part-level design generation. On PubCADBench and RealCADBench, VisCAD-M1 achieves the highest average part-level score among the evaluated models, reaching 0.5540 compared with 0.5496 for the strongest frontier model. Reusing VisCAD-M1 as a test-time verifier can further raise the score to 0.5797, an approximately 5 percent relative improvement over the previous state of the art. VisCAD also includes a domain-specific harness that leverages frontier models for complex assembly generation and demonstrates advantages over general-purpose harnesses in both quantitative and qualitative evaluations.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 31 references that are in the map. First posted on arXiv 3 Sep 2026.

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17 of 31 references.

References · 17

  1. 2018ABC: A Big CAD Model Dataset For Geometric Deep Learningcited by 341
  2. 2021DeepCAD: A Deep Generative Network for Computer-Aided Design Modelscited by 271
  3. 2024BrepGen: A B-rep Generative Diffusion Model with Structured Latent Geometrycited by 103
  4. 2021BRepNet: A topological message passing system for solid modelscited by 95
  5. 2020SketchGraphs: A Large-Scale Dataset for Modeling Relational Geometry in Computer-Aided Designcited by 72
  6. 2025CAD-Llama: Leveraging Large Language Models for Computer-Aided Design Parametric 3D Model Generationcited by 67
  7. 2024CAD-Recode: Reverse Engineering CAD Code from Point Cloudscited by 66
  8. 2026Seed2.0 Model Card: Towards Intelligence Frontier for Real-World Complexitycited by 58
  9. 2024CadVLM: Bridging Language and Vision in the Generation of Parametric CAD Sketchescited by 28
  10. 2024OpenECAD: An Efficient Visual Language Model for Editable 3D-CAD Designcited by 26
  11. 2025Large Language Models for Computer-Aided Design: A Surveycited by 18
  12. 2026BenchCAD: A Comprehensive, Industry-Standard Benchmark for Programmatic CADcited by 15
5 more
  1. 2026CADBench: A Multimodal Benchmark for AI-Assisted CAD Program Generationcited by 11
  2. 2024PICASSO: A Feed-Forward Framework for Parametric Inference of CAD Sketches via Rendering Self-Supervisioncited by 9
  3. 2026P3D-Bench: Benchmarking MLLMs for Parametric 3D Generation and Structural Reasoningcited by 9
  4. 2025GenCAD-3D: CAD Program Generation using Multimodal Latent Space Alignment and Synthetic Dataset Balancingcited by 8
  5. 2025GenCAD-Self-Repairing: Feasibility Enhancement for 3D CAD Generationcited by 2

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