Popular GitHub Repositories for Cad

[NeurIPS'24 Spotlight] Text2CAD: Generating Sequential CAD Designs from Beginner-to-Expert Level Text Prompts

[ICCV 2023 R6D] PyTorch implementation of CNOS: A Strong Baseline for CAD-based Novel Object Segmentation based on Segmenting Anything and DINOv2

N2D2 is an open source CAD framework for Deep Neural Network simulation and full DNN-based applications building.

CAD-Recode: Reverse Engineering CAD Code from Point Clouds

[ICML 2023] Official PyTorch Implementation of "Hierarchical Neural Coding for Controllable CAD Model Generation".

BlenderLLM: A LLM specifically designed to generate CAD scripts based on user instructions. These scripts are then executed in Blender to render 3D models.

generate physically realistic synthetic dataset of cluttered scenes using 3D CAD models to train CNN based object detectors

This n8n workflow enables automated conversion of CAD files (such as `.rvt`, `.ifc`, `.dwg`) using command-line converters on a local Windows machine

FreeCADMCP 通过模型上下文协议 (MCP) 将 FreeCAD 连接到 Claude AI 和其他支持 MCP 的工具（如 Cursor），使 Claude 能够直接交互并控制 FreeCAD。这种集成实现了提示辅助的 3D CAD 设计。

这是一个基于模型上下文协议 (MCP) 的 AutoCAD 集成服务器，能让用户用自然语言操控 AutoCAD。借助这个服务器，用户可以利用 Claude 等大型语言模型创建、修改和分析 AutoCAD 图纸，还能存储和查询 CAD 元素的相关数据。目前项目处于学习阶段，只实现了端到端的通信，具体的工具函数尚未完成。

Computation of Midcurve of Thin Polygons using Neural Networks

This n8n workflow enables automated conversion of CAD files (such as `.rvt`, `.ifc`, `.dwg`) using command-line converters on a local Windows machine

​This repository provides code and resources for automating manufacturing feature recognition in CAD designs using vision-language models.

This is the github project for the F1Tenth Independent Study Projects 2021. In this project we want to plan the paths for multi-agent given a prior of an environment like a blueprint of the building’s floor plan.

一个允许Claude在Rayon上绘制CAD图纸的MCP服务器

This project allows users to generate synthetic videos from CAD models, including .npy files with additional information. Models are loaded dynamically into a Blender scene, and the camera smoothly moves along spherical points to create the final video.

Text to CAD using LLMs and BadCAD

Engineering resources for makers and independent learners.

Investigated Meta Pseudo Labels for breast cancer CAD. The pipeline failed because the baseline segmentation model could not learn from the extremely limited labeled data. This research demonstrates that it's success is contingent on first establishing a functional supervised model, a step that was ultimately hampered by severe data scarcity