arXiv AI

SOV-CAD: Stepwise Orthographic Views Guided CAD Modeling Sequence Reconstruction

arXiv:2607. 04119v1 Announce Type: cross Abstract: Reconstructing Computer-Aided Design (CAD) modeling sequences from images is crucial for preserving design intent and supporting parametric editing.

arXiv Machine Learning
Jun 3

CADFit: Precise Mesh-to-CAD Program Generation with Hybrid Optimization

arXiv:2605. 01171v2 Announce Type: replace-cross Abstract: Despite recent progress, recovering parametric CAD construction sequences from geometric input, such as meshes or point clouds, is a key challenge for design and manufacturing, as existing CAD reconstruction and generation methods are largely restricted to difficult-to-edit formats like meshes or Breps or editable simple sketch-and-extrude pipelines and low-complexity datasets.

By Ghadi Nehme, Eamon Whalen, Faez Ahmed
arXiv AI
Jun 12

IterCAD: An Iterative Multimodal Agent for Visually-Grounded CAD Generation and Editing

arXiv:2606. 13368v1 Announce Type: new Abstract: Computer-Aided Design is pivotal in modern manufacturing, yet existing automated methods predominantly rely on open-loop, one-shot generation, creating a mismatch with iterative real-world practices.

By Tao Hu, Jiaxin Ai, Licheng Wen, Xueheng Li, Shu Zou, Siqi Li, Nianchen Deng, Xinyu Cai, Hongbin Zhou, Pinlong Cai, Daocheng Fu, Yu Yang, Hairong Zhang, Botian Shi, Xuemeng Yang
Hugging Face Trending Papers
Jun 11

IterCAD: An Iterative Multimodal Agent for Visually-Grounded CAD Generation and Editing

Computer-Aided Design is pivotal in modern manufacturing, yet existing automated methods predominantly rely on open-loop, one-shot generation, creating a mismatch with iterative real-world practices. In this paper, we present IterCAD, a unified multimodal agent framework for closed-loop, interactive CAD generation and editing.

arXiv Machine Learning
Sep 2

CADKnitter: Compositional CAD Generation from Text and Geometry Guidance

CADKnitter is a compositional CAD generation framework that uses geometric-guiding cues to steer diffusion sampling, enabling the creation of complementary CAD parts that satisfy both geometric constraints of an existing model and semantic constraints from a text prompt. The authors introduce KnitCAD, a dataset of over 310,000 CAD models paired with textual prompts and assembly metadata to support training and evaluation. Experiments show that CADKnitter outperforms state‑of‑the‑art baselines by a clear margin.

By Tri Le, Khang Nguyen, Baoru Huang, Tung D. Ta, Anh Nguyen
arXiv AI
Aug 26

IterCAD: Iterative Program Repair for CAD Code Generation from Orthographic Views

IterCAD introduces an iterative program‑repair framework for generating executable parametric CAD code from dimension‑annotated orthographic drawings. Unlike one‑shot vision‑language models, IterCAD repeatedly evaluates the current CAD output, identifies discrepancies with the target views, and decides whether to revise the code or stop refining. The authors build a structured revise‑or‑stop dataset (IterCAD‑RS) and a three‑stage training pipeline—initial generation, revision learning, and multi‑turn reinforcement learning—to enable this progressive correction, achieving higher code executability and geometric fidelity on the CADExpert benchmark.

By Yuchuan Wu, Ke Niu, Haiyang Yu, Zhuofan Chen, Xiangyang Xue, Bin Li
arXiv Computer Vision
Sep 21

VGGT-CAD: Reconstructing Parametric CAD 3D Model with Geometric Grounding

VGGT-CAD is a geometry‑aware framework that reconstructs parametric CAD 3D models from single and multi‑view images. It incorporates pretrained 3D geometric priors by encoding camera parameters as condition tokens and jointly modeling them with image tokens. The method introduces a variable‑view cross‑view context aggregation module and a training‑free geometry‑aware view selection strategy, and decodes the learned representation into CAD command sequences using a non‑autoregressive decoder. Additionally, VideoCAD, a large‑scale multi‑view video benchmark derived from existing CAD data, is presented to evaluate the approach.

By Chunan Yu, Tianrun Chen, Fu Shen, Cheng Chen, Lanyun Zhu, Yang Yang
arXiv AI
Jul 28

DreamCAD: Scaling Multi-modal CAD Generation using Differentiable Parametric Surfaces

arXiv:2603. 05607v2 Announce Type: replace-cross Abstract: Computer-Aided Design (CAD) relies on structured and editable geometric representations, yet existing generative methods are constrained by small annotated datasets with explicit design histories or boundary representation (BRep) labels.

By Mohammad Sadil Khan, Muhammad Usama, Rolandos Alexandros Potamias, Didier Stricker, Muhammad Zeshan Afzal, Jiankang Deng, Ismail Elezi
arXiv AI
Jul 1

Embodied CAD: Solver-Grounded LLM Agents for Parametric B-Rep Assembly Modeling

arXiv:2606. 31252v1 Announce Type: new Abstract: Large language models can write plausible CAD scripts, but reliable industrial CAD modeling requires more than syntactically valid code: every feature, placement, and assembly relation must be accepted by an exact geometric kernel while remaining editable as parametric boundary representation geometry.

By Fumin Liu, Haoyu Zhou, Fei Hao, Lin Yang
Hugging Face Trending Papers
Jul 30

Drawing-Recode: Annotation Grounding for Parametric CAD Code Generation from Raster 2D CAD Drawings

Recovering Parametric CAD sequences from raster-format 2D Computer-Aided Design (CAD) drawings accumulated prior to digital transformation is important for part reproduction and manufacturing process automation. However, existing studies either process only vector drawings or are limited to specific domains, and fail to explicitly connect dimensional annotations to geometric information, limiting their use of dimensional information for 3D Parametric CAD sequences recovery.