CADReview: Automatically Reviewing CAD Programs with Error Detection and Correction

• URL: http://arxiv.org/abs/2505.22304v1
• Date: Wed, 28 May 2025 12:41:00 GMT
• Title: CADReview: Automatically Reviewing CAD Programs with Error Detection and Correction
• Authors: Jiali Chen, Xusen Hei, HongFei Liu, Yuancheng Wei, Zikun Deng, Jiayuan Xie, Yi Cai, Li Qing,
• Abstract summary: We introduce the CAD review task to automatically detect and correct potential errors.<n>In this paper, we propose the CAD program repairer (ReCAD) framework to effectively detect program errors.<n>We create a dataset, CADReview, consisting of over 20K program-image pairs, with diverse errors for the CAD review task.
• Score: 11.33947758511237
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• RAG-6DPose: Retrieval-Augmented 6D Pose Estimation via Leveraging CAD as Knowledge Base [112.72361202480154]
  We present RAG-6DPose, a retrieval-augmented approach that leverages 3D CAD models as a knowledge base.<n> Experimental results on standard benchmarks and real-world robotic tasks demonstrate the effectiveness and robustness of our approach.
  arXiv  Detail & Related papers  (2025-06-23T17:19:41Z)
• CReFT-CAD: Boosting Orthographic Projection Reasoning for CAD via Reinforcement Fine-Tuning [50.867869718716555]
  We introduce CReFT-CAD, a two-stage fine-tuning paradigm that first employs a curriculum-driven reinforcement learning stage with difficulty-aware rewards to build reasoning ability steadily.<n>We release TriView2CAD, the first large-scale, open-source benchmark for orthographic projection reasoning.
  arXiv  Detail & Related papers  (2025-05-31T13:52:56Z)
• CADCrafter: Generating Computer-Aided Design Models from Unconstrained Images [69.7768227804928]
  CADCrafter is an image-to-parametric CAD model generation framework that trains solely on synthetic textureless CAD data.<n>We introduce a geometry encoder to accurately capture diverse geometric features.<n>Our approach can robustly handle real unconstrained CAD images, and even generalize to unseen general objects.
  arXiv  Detail & Related papers  (2025-04-07T06:01:35Z)
• CAD-Assistant: Tool-Augmented VLLMs as Generic CAD Task Solvers [12.5472026454031]
  CAD-Assistant addresses user queries by generating actions that are iteratively executed on a Python interpreter equipped with the FreeCAD software.<n>We consider a wide range of CAD-specific tools including a sketch image parameterizer, rendering modules, a 2D cross-section generator, and other specialized routines.
  arXiv  Detail & Related papers  (2024-12-18T12:57:56Z)
• Generating CAD Code with Vision-Language Models for 3D Designs [6.532952167132679]
  We introduce CADCodeVerify, a novel approach to iteratively verify and improve 3D objects generated from CAD code.<n>Our approach works by producing ameliorative feedback by prompting a Vision-Language Model to generate and answer a set of validation questions.<n>Our findings show that CADCodeVerify improves VLM performance by providing visual feedback, enhancing the structure of the 3D objects, and increasing the success rate of the compiled program.
  arXiv  Detail & Related papers  (2024-10-07T02:44:50Z)
• PS-CAD: Local Geometry Guidance via Prompting and Selection for CAD Reconstruction [86.726941702182]
  We introduce geometric guidance into the reconstruction network PS-CAD. We provide the geometry of surfaces where the current reconstruction differs from the complete model as a point cloud. Second, we use geometric analysis to extract a set of planar prompts, that correspond to candidate surfaces.
  arXiv  Detail & Related papers  (2024-05-24T03:43:55Z)
• SECAD-Net: Self-Supervised CAD Reconstruction by Learning Sketch-Extrude Operations [21.000539206470897]
  SECAD-Net is an end-to-end neural network aimed at reconstructing compact and easy-to-edit CAD models. We show superiority over state-of-the-art alternatives including the closely related method for supervised CAD reconstruction.
  arXiv  Detail & Related papers  (2023-03-19T09:26:03Z)
• AutoCAD: Automatically Generating Counterfactuals for Mitigating Shortcut Learning [70.70393006697383]
  We present AutoCAD, a fully automatic and task-agnostic CAD generation framework. In this paper, we present AutoCAD, a fully automatic and task-agnostic CAD generation framework.
  arXiv  Detail & Related papers  (2022-11-29T13:39:53Z)
• Patch2CAD: Patchwise Embedding Learning for In-the-Wild Shape Retrieval from a Single Image [58.953160501596805]
  We propose a novel approach towards constructing a joint embedding space between 2D images and 3D CAD models in a patch-wise fashion. Our approach is more robust than state of the art in real-world scenarios without any exact CAD matches.
  arXiv  Detail & Related papers  (2021-08-20T20:58:52Z)
• Fusion 360 Gallery: A Dataset and Environment for Programmatic CAD Construction from Human Design Sequences [43.57844212541765]
  We present the Fusion 360 Gallery, consisting of a simple language with just the sketch and extrude modeling operations. We also present an interactive environment called the Fusion 360 Gym, which exposes the sequential construction of a CAD program as a Markov decision process.
  arXiv  Detail & Related papers  (2020-10-05T23:18:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.