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awesome-CAD
😎 A list of awesome Computer-Aided Design (CAD) papers
https://github.com/bertjiazheng/awesome-CAD
Last synced: about 4 hours ago
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Datasets
- Fusion 360 Gallery: A Dataset and Environment for Programmatic CAD Construction from Human Design Sequences
- PVDeconv: Point-voxel deconvolution for autoencoding cad construction in 3D - dataset/)] |
- SketchGraphs: A Large-Scale Dataset for Modeling Relational Geometry in Computer-Aided Design
- ABC: A Big CAD Model Dataset For Geometric Deep Learning - geometry.github.io/abc-dataset/)] |
- A Large-scale Annotated Mechanical Components Benchmark for Classification and Retrieval Tasks with Deep Neural Networks (MCB) - large-scale-annotated-mechanical-components-benchmark-for-classification-and-retrieval-tasks-with-deep-neural-networks/)] |
- AutoMate: A Dataset and Learning Approach for Automatic Mating of CAD Assemblies
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CAD Reconstruction
- SolidGen: An Autoregressive Model for Direct B-rep Synthesis
- Reconstructing Editable Prismatic CAD from Rounded Voxel Models
- [project
- Point2Cyl: Reverse Engineering 3D Objects from Point Clouds to Extrusion Cylinders
- PC2WF: 3D Wireframe Reconstruction from Raw Point Clouds
- PIE-NET: Parametric Inference of Point Cloud Edges - NET)] |
- ParSeNet: A Parametric Surface Fitting Network for 3D Point Clouds - codebase)] |
- Supervised Fitting of Geometric Primitives to 3D Point Clouds
- PlankAssembly: Robust 3D Reconstruction from Three Orthographic Views with Learnt Shape Programs - research.github.io/PlankAssembly/)] [[code](https://github.com/manycore-research/PlankAssembly/)] |
- ComplexGen: CAD Reconstruction by B-Rep Chain Complex Generation
- From 2D CAD Drawings to 3D Parametric Models: A Vision-Language Approach - research.github.io/CAD2Program/)] |
- CAD-Recode: Reverse Engineering CAD Code from Point Clouds
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CAD Generation
- 3DALL-E: Integrating Text-to-Image AI in 3D Design Workflows
- [project - li/Free2CAD)] |
- SkexGen: Autoregressive Generation of CAD Construction Sequences with Disentangled Codebooks
- Vitruvion: A Generative Model of Parametric CAD Sketches
- JoinABLe: Learning Bottom-up Assembly of Parametric CAD Joints
- SketchGen: Generating Constrained CAD Sketches
- Computer-Aided Design as Language - research/blob/master/cadl)] |
- DeepCAD: A Deep Generative Network for Computer-Aided Design Models
- Engineering Sketch Generation for Computer-Aided Design
- Sketch2CAD: Sequential CAD Modeling by Sketching in Context - li/Sketch2CAD)] |
- PolyGen: An Autoregressive Generative Model of 3D Meshes - research/blob/master/polygen/)] |
- Text2CAD: Text to 3D CAD Generation via Technical Drawings - project/)] |
- FlexCAD: Unified and Versatile Controllable CAD Generation with Fine-tuned Large Language Models
- CadVLM: Bridging Language and Vision in the Generation of Parametric CAD Sketches
- BrepGen: A B-rep Generative Diffusion Model with Structured Latent Geometry
- Don’t Mesh with Me: Generating Constructive Solid Geometry Instead of Meshes by Fine-Tuning a Code-Generation LLM
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CAD Representation
- DualCSG: Learning Dual CSG Trees for General and Compact CAD Modeling
- Discovering Design Concepts for CAD Sketches
- Self-Supervised Representation Learning for CAD
- CSG-Stump: A Learning Friendly CSG-Like Representation for Interpretable Shape Parsing
- UV-Net: Learning from Boundary Representations - Net)] |
- BRepNet: A Topological Message Passing System for Solid Models
- CSGNet: Neural Shape Parser for Constructive Solid Geometry
- CADOps-Net: Jointly Learning CAD Operation Types and Steps from Boundary-Representations
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CAD Recognition
- CADTransformer: Panoptic Symbol Spotting Transformer for CAD Drawings - Group/CADTransformer)] |
- GAT-CADNet: Graph Attention Network for Panoptic Symbol Spotting in CAD Drawings
- FloorPlanCAD: A Large-Scale CAD Drawing Dataset for Panoptic Symbol Spotting
- Symbol as Points: Panoptic Symbol Spotting via Point-based Representation
- VectorFloorSeg: Two-Stream Graph Attention Network for Vectorized Roughcast Floorplan Segmentation
- CADSpotting: Robust Panoptic Symbol Spotting on Large-Scale CAD Drawings
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