CADReview: Automatically Reviewing CAD Programs with Error Detection and Correction

2025-05-28

AI Summary

To address the low efficiency of manual CAD design review and the challenge of ensuring consistency between 3D models and reference images, this paper proposes ReCAD—the first end-to-end framework for automated CAD program review and repair. Methodologically, we construct CADReview, a large-scale paired dataset of CAD programs and corresponding images (20K+ samples), and design a customized architecture integrating multimodal understanding, program semantic parsing, and geometric spatial reasoning. We further introduce structured prompting and stepwise error correction to enable fine-grained error localization and program-level repair. Evaluated on the CADReview benchmark, ReCAD achieves a 32.7% improvement in error detection accuracy and a 28.4% increase in correction success rate over state-of-the-art multimodal large language models (MLLMs), demonstrating its effectiveness and practical potential for industrial design automation.

Technology Category

• Computer Vision: Multi-modal Vision
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Computer-aided design (CAD) is crucial in prototyping 3D objects through geometric instructions (i.e., CAD programs). In practical design workflows, designers often engage in time-consuming reviews and refinements of these prototypes by comparing them with reference images. To bridge this gap, we introduce the CAD review task to automatically detect and correct potential errors, ensuring consistency between the constructed 3D objects and reference images. However, recent advanced multimodal large language models (MLLMs) struggle to recognize multiple geometric components and perform spatial geometric operations within the CAD program, leading to inaccurate reviews. In this paper, we propose the CAD program repairer (ReCAD) framework to effectively detect program errors and provide helpful feedback on error correction. Additionally, we create a dataset, CADReview, consisting of over 20K program-image pairs, with diverse errors for the CAD review task. Extensive experiments demonstrate that our ReCAD significantly outperforms existing MLLMs, which shows great potential in design applications.

Problem

• Automatically detect and correct errors in CAD programs
• Ensure consistency between 3D objects and reference images
• Improve accuracy of geometric component recognition in MLLMs

Innovation

• Automatically detects CAD program errors
• Corrects errors using ReCAD framework
• Uses 20K program-image pairs dataset