5 papers
Towards High-Fidelity CAD Generation via LLM-Driven Program Generation and Text-Based B-Rep Primitive Grounding
Jiahao Li, Qingwang Zhang, Qiuyu Chen +3
The field of Computer-Aided Design (CAD) generation has made significant progress in recent years. Existing methods typically fall into two separate categories: parametric CAD mode…
Mamba-CAD: State Space Model For 3D Computer-Aided Design Generative Modeling
Xueyang Li, Yunzhong Lou, Yu Song +1
Computer-Aided Design (CAD) generative modeling has a strong and long-term application in the industry. Recently, the parametric CAD sequence as the design logic of an object has b…
Seek-CAD: A Self-refined Generative Modeling for 3D Parametric CAD Using Local Inference via DeepSeek
Xueyang Li, Jiahao Li, Yu Song +2
The advent of Computer-Aided Design (CAD) generative modeling will significantly transform the design of industrial products. The recent research endeavor has extended into the rea…
ReCAD: Reinforcement Learning Enhanced Parametric CAD Model Generation with Vision-Language Models
Jiahao Li, Yusheng Luo, Yunzhong Lou +1
We present ReCAD, a reinforcement learning (RL) framework that bootstraps pretrained large models (PLMs) to generate precise parametric computer-aided design (CAD) models from mult…
CAD-Llama: Leveraging Large Language Models for Computer-Aided Design Parametric 3D Model Generation
Jiahao Li, Weijian Ma, Xueyang Li +3
Recently, Large Language Models (LLMs) have achieved significant success, prompting increased interest in expanding their generative capabilities beyond general text into domain-sp…