GeoCAD: Local Geometry-Controllable CAD Generation with Large Language Models
Zhanwei Zhang, Kaiyuan Liu, Junjie Liu, Wenxiao Wang, Binbin Lin, Liang Xie, Chen Shen, Deng Cai
摘要
Local geometry-controllable computer-aided design (CAD) generation aims to modify local parts of CAD models automatically, enhancing design efficiency. It also ensures that the shapes of newly generated local parts follow user-specific geometric instructions (e.g., an isosceles right triangle or a rectangle with one corner cut off). However, existing methods encounter challenges in achieving this goal. Specifically, they either lack the ability to follow textual instructions or are unable to focus on the local parts. To address this limitation, we introduce GeoCAD, a user-friendly and local geometry-controllable CAD generation method. Specifically, we first propose a complementary captioning strategy to generate geometric instructions for local parts. This strategy involves vertex-based and VLLM-based captioning for systematically annotating simple and complex parts, respectively. In this way, we caption 221k different local parts in total. In the training stage, given a CAD model, we randomly mask a local part. Then, using its geometric instruction and the remaining parts as input, we prompt large language models (LLMs) to predict the masked part. During inference, users can specify any local part for modification while adhering to a variety of predefined geometric instructions. Extensive experiments demonstrate the effectiveness of GeoCAD in generation quality, validity and text-to-CAD consistency. Code will be available at https://github.com/Zhanwei-Z/GeoCAD.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper3
- CAD-Coder: Text-to-CAD Generation with Chain-of-Thought and Geometric RewardYandong Guan, Xilin Wang, Ximing Xing, Jing Zhang 等NeurIPS 2025 · 被引用 64 次
- TokenSqueeze: Performance-Preserving Compression for Reasoning LLMsYuxiang Zhang, Zhengxu Yu, Weihang Pan, Zhongming Jin 等NeurIPS 2025 · 被引用 6 次
- Bridging Tokens and Geometry: Token-wise 3D Supervision for CAD GenerationYijia Guan, Jianhua SunICML 2026
它引用的顶会 Paper32
- LoRA: Low-Rank Adaptation of Large Language ModelsEdward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu 等ICLR 2022 · 被引用 18,833 次
- Visual Instruction TuningHaotian Liu, Chunyuan Li, Qingyang Wu, Yong Jae LeeNeurIPS 2023 · 被引用 11,349 次
- DeepCAD: A Deep Generative Network for Computer-Aided Design ModelsRundi Wu, Chang Xiao, Changxi ZhengICCV 2021 · 被引用 290 次
- LLaMA-Adapter: Efficient Fine-tuning of Large Language Models with Zero-initialized AttentionRenrui Zhang, Jiaming Han, Chris Liu, Aojun Zhou 等ICLR 2024 · 被引用 174 次
- IMAGPose: A Unified Conditional Framework for Pose-Guided Person GenerationFei Shen, Jinhui TangNeurIPS 2024 · 被引用 172 次
相关 Paper
- FlexCAD: Unified and Versatile Controllable CAD Generation with Fine-tuned Large Language ModelsZhanwei Zhang, Shizhao Sun, Wenxiao Wang, Deng Cai 等ICLR 2025
- CAD-Editor: A Locate-then-Infill Framework with Automated Training Data Synthesis for Text-Based CAD EditingYu Yuan, Shizhao Sun, Qi Liu, Jiang BianICML 2025
- Pointer-CAD: Unifying B-Rep and Command Sequences via Pointer-based Edges & Faces SelectionDacheng Qi, Chenyu Wang, Jingwei Xu, Tianzhe Chu 等CVPR 2026 · 被引用 10 次
- FreeCAD: A Multimodal Framework for 3D CAD Model Generation from Free-Form PromptsDawei Lin, Meng Yuan, Ziming Wang, Tieru Wu 等ACM MM 2025 · 被引用 4 次
- CAD-Llama: Leveraging Large Language Models for Computer-Aided Design Parametric 3D Model GenerationJiahao Li, Weijian Ma, Xueyang Li, Yunzhong Lou 等CVPR 2025
