GeoCAD: Local Geometry-Controllable CAD Generation with Large Language Models
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Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 42 of its 64 references that are in the map. First posted on arXiv 12 Jun 2025.
Builds on
42 of 64 references.
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30 more
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Built on by
Citing papers per year: 2025: 2; 2026: 2.
4 papers.
- 2025ReCAD: Reinforcement Learning Enhanced Parametric CAD Model Generation with Vision-Language Modelscited by 10
- 2025TokenSqueeze: Performance-Preserving Compression for Reasoning LLMscited by 5
- 2026PR-CAD: Progressive Refinement for Unified Controllable and Faithful Text-to-CAD Generation with Large Language Modelscited by 3
- 2026RA-CAD: Learning Post-Execution Critique for State-Aware Text-to-CAD Generationcited by 0
- 2026RA-CAD: Learning Post-Execution Critique for State-Aware Text-to-CAD Generation6 Aug 2026
- 2026PR-CAD: Progressive Refinement for Unified Controllable and Faithful Text-to-CAD Generation with Large Language Models27 Mar 2026
- 2025ReCAD: Reinforcement Learning Enhanced Parametric CAD Model Generation with Vision-Language Models6 Dec 2025
- 2025TokenSqueeze: Performance-Preserving Compression for Reasoning LLMs17 Nov 2025