{"id":13992926,"url":"https://github.com/bertjiazheng/Awesome-CAD","last_synced_at":"2025-07-22T16:32:56.678Z","repository":{"id":111946688,"uuid":"502311087","full_name":"bertjiazheng/Awesome-CAD","owner":"bertjiazheng","description":"😎 A list of awesome Computer-Aided Design (CAD) 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CAD Research \u0026 Related","Attribution"],"sub_categories":["Mesh Processing \u0026 Boolean Engines","AI-Assisted CG Software"],"readme":"# Awesome CAD [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)\n\nA curated list of awesome Computer-Aided Design (CAD) papers, inspired by [awesome-computer-vision](https://github.com/jbhuang0604/awesome-computer-vision).\n\n## Survey\n| Papers | Venue | Links |\n|--------|-------|-------|\n| [Large Language Models for Computer-Aided Design: A Survey](https://arxiv.org/abs/2505.08137) | arXiv 2025 | [[project]](https://github.com/lichengzhanguom/LLMs-CAD-Survey-Taxonomy) |\n\n## Datasets\n\n| Papers | Venue | Links |\n|--------|-------|-------|\n| [CADOps-Net: Jointly Learning CAD Operation Types and Steps from Boundary-Representations](https://arxiv.org/abs/2208.10555) | 3DV 2022 | [[project](https://cvi2.uni.lu/cc3d-ops/)] |\n| [Fusion 360 Gallery: A Dataset and Environment for Programmatic CAD Construction from Human Design Sequences](https://arxiv.org/abs/2010.02392) | SIGGRAPH 2021 | [[project](https://github.com/AutodeskAILab/Fusion360GalleryDataset)] |\n| [AutoMate: A Dataset and Learning Approach for Automatic Mating of CAD Assemblies](https://arxiv.org/abs/2105.12238) | SIGGRAPH Asia 2021 | [[project](https://github.com/deGravity/automate)] |\n| [PVDeconv: Point-voxel deconvolution for autoencoding cad construction in 3D](https://arxiv.org/abs/2101.04493) | ICIP 2020 | [[project](https://cvi2.uni.lu/cc3d-dataset/)] |\n| [SketchGraphs: A Large-Scale Dataset for Modeling Relational Geometry in Computer-Aided Design](https://arxiv.org/abs/2007.08506) | ICML Workshop 2020 | [[project]](https://github.com/PrincetonLIPS/SketchGraphs) |\n| [A Large-scale Annotated Mechanical Components Benchmark for Classification and Retrieval Tasks with Deep Neural Networks (MCB)](https://www.cs.utexas.edu/~huangqx/mcb_benchmark.pdf) | ECCV 2020 | [[project](https://engineering.purdue.edu/cdesign/wp/a-large-scale-annotated-mechanical-components-benchmark-for-classification-and-retrieval-tasks-with-deep-neural-networks/)] |\n| [ABC: A Big CAD Model Dataset For Geometric Deep Learning](https://arxiv.org/abs/1812.06216) | CVPR 2019 | [[project](https://deep-geometry.github.io/abc-dataset/)] |\n\n## CAD Reconstruction\n\n| Papers | Venue | Links |\n|--------|-------|-------|\n| [CAD-GPT: Synthesising CAD Construction Sequence with Spatial Reasoning-Enhanced Multimodal LLMs](https://arxiv.org/abs/2412.19663) | arXiv 2025 | |\n| [CAD-Recode: Reverse Engineering CAD Code from Point Clouds](https://arxiv.org/abs/2412.14042) | arXiv 2025 | |\n| [From 2D CAD Drawings to 3D Parametric Models: A Vision-Language Approach](https://arxiv.org/abs/2412.11892) | AAAI 2025 | [[project](https://manycore-research.github.io/CAD2Program/)] |\n| [PlankAssembly: Robust 3D Reconstruction from Three Orthographic Views with Learnt Shape Programs](https://arxiv.org/abs/2308.05744) | ICCV 2023 | [[project](https://manycore-research.github.io/PlankAssembly/)] [[code](https://github.com/manycore-research/PlankAssembly/)] |\n| [SolidGen: An Autoregressive Model for Direct B-rep Synthesis](https://arxiv.org/abs/2203.13944) | TMLR 2023 | |\n| [Reconstructing Editable Prismatic CAD from Rounded Voxel Models](https://arxiv.org/abs/2209.01161) | SIGGRAPH Asia 2022 | |\n| [ComplexGen: CAD Reconstruction by B-Rep Chain Complex Generation](https://arxiv.org/abs/2205.14573) | SIGGRAPH 2022 | [[project](https://haopan.github.io/complexgen.html)] [[code](https://github.com/guohaoxiang/ComplexGen)] |\n| [Point2Cyl: Reverse Engineering 3D Objects from Point Clouds to Extrusion Cylinders](https://arxiv.org/abs/2112.09329) | CVPR 2022 | [[project](https://point2cyl.github.io/)] [[code](https://github.com/mikacuy/point2cyl)] |\n| [PC2WF: 3D Wireframe Reconstruction from Raw Point Clouds](https://arxiv.org/abs/2103.02766) | ICLR 2021 | [[code](https://github.com/YujiaLiu76/PC2WF)] |\n| [PIE-NET: Parametric Inference of Point Cloud Edges](https://arxiv.org/abs/2007.04883) | NeurIPS 2020 | [[code](https://github.com/wangxiaogang866/PIE-NET)] |\n| [ParSeNet: A Parametric Surface Fitting Network for 3D Point Clouds](https://arxiv.org/abs/2003.12181) | ECCV 2020 | [[project](https://hippogriff.github.io/parsenet/)] [[code](https://github.com/Hippogriff/parsenet-codebase)] |\n| [Supervised Fitting of Geometric Primitives to 3D Point Clouds](https://arxiv.org/abs/1811.08988) | CVPR 2019 | [[code](https://github.com/lingxiaoli94/SPFN)] |\n\n## CAD Generation\n\n| Papers | Venue | Links |\n|--------|-------|-------|\n| [Text-to-CadQuery: A New Paradigm for CAD Generation with Scalable Large Model Capabilities](https://www.arxiv.org/abs/2505.06507) | arXiv 2025 | [[code](https://github.com/Text-to-CadQuery/Text-to-CadQuery)] |\n| [FlexCAD: Unified and Versatile Controllable CAD Generation with Fine-tuned Large Language Models](https://arxiv.org/abs/2411.05823) | ICLR 2025 | | \n| [Don’t Mesh with Me: Generating Constructive Solid Geometry Instead of Meshes by Fine-Tuning a Code-Generation LLM](https://arxiv.org/abs/2411.15279) | arXiv 2024 | |\n| [Text2CAD: Text to 3D CAD Generation via Technical Drawings](https://arxiv.org/abs/2411.06206) | NeurIPS 2024 | [[project](https://sadilkhan.github.io/text2cad-project/)] |\n| [CadVLM: Bridging Language and Vision in the Generation of Parametric CAD Sketches](https://arxiv.org/abs/2409.17457) | NeurIPS Workshop 2024 | |\n| [BrepGen: A B-rep Generative Diffusion Model with Structured Latent Geometry](https://arxiv.org/abs/2401.15563) | SIGGRAPH 2024 | [[code](https://github.com/samxuxiang/BrepGen)] |\n| [3DALL-E: Integrating Text-to-Image AI in 3D Design Workflows](https://arxiv.org/abs/2210.11603)| arXiv 2022 | |\n| Free2CAD: Parsing Freehand Drawings into CAD Commands | SIGGRAPH 2022 | [[project](https://geometry.cs.ucl.ac.uk/projects/2022/free2cad/)] [[code](https://github.com/Enigma-li/Free2CAD)] |\n| [SkexGen: Autoregressive Generation of CAD Construction Sequences with Disentangled Codebooks](https://arxiv.org/abs/2207.04632) | ICML 2022 | [[project](https://samxuxiang.github.io/skexgen)] [[code](https://github.com/samxuxiang/SkexGen)] |\n| [Vitruvion: A Generative Model of Parametric CAD Sketches](https://arxiv.org/abs/2109.14124) | ICLR 2022 | [[project]](https://lips.cs.princeton.edu/vitruvion/) [[code](https://github.com/PrincetonLIPS/vitruvion)] |\n| [JoinABLe: Learning Bottom-up Assembly of Parametric CAD Joints](https://arxiv.org/abs/2111.12772) | CVPR 2022 | [[code](https://github.com/AutodeskAILab/JoinABLe)] |\n| [SketchGen: Generating Constrained CAD Sketches](https://arxiv.org/abs/2106.02711) | NeurIPS 2021 | |\n| [Computer-Aided Design as Language](https://arxiv.org/abs/2105.02769) | NeurIPS 2021 | [[data](http://github.com/deepmind/deepmind-research/blob/master/cadl)] |\n| [DeepCAD: A Deep Generative Network for Computer-Aided Design Models](https://arxiv.org/abs/2105.09492) | ICCV 2021 | [[project](http://www.cs.columbia.edu/cg/deepcad/)] [[code](https://github.com/ChrisWu1997/DeepCAD)] |\n| [Engineering Sketch Generation for Computer-Aided Design](https://arxiv.org/abs/2104.09621) | CVPR Workshop 2021 | |\n| [Sketch2CAD: Sequential CAD Modeling by Sketching in Context](https://arxiv.org/abs/2009.04927) | SIGGRAPH Asia 2020 | [[project](http://geometry.cs.ucl.ac.uk/projects/2020/sketch2cad/)] [[code](https://github.com/Enigma-li/Sketch2CAD)] |\n| [PolyGen: An Autoregressive Generative Model of 3D Meshes](https://arxiv.org/abs/2002.10880) | ICML 2020 | [[code](https://github.com/deepmind/deepmind-research/blob/master/polygen/)] |\n\n## CAD Representation\n\n| Papers | Venue | Links |\n|--------|-------|-------|\n| [DualCSG: Learning Dual CSG Trees for General and Compact CAD Modeling](https://arxiv.org/abs/2301.11497) | arXiv 2023 | |\n| [Discovering Design Concepts for CAD Sketches](https://arxiv.org/abs/2210.14451) | NeurIPS 2022 | [[code](https://github.com/yyuezhi/SketchConcept)] |\n| [Self-Supervised Representation Learning for CAD](https://arxiv.org/abs/2210.10807) | CVPR 2023 | |\n| [CADOps-Net: Jointly Learning CAD Operation Types and Steps from Boundary-Representations](https://arxiv.org/abs/2208.10555) | 3DV 2022 | |\n| [CSG-Stump: A Learning Friendly CSG-Like Representation for Interpretable Shape Parsing](https://arxiv.org/abs/2108.11305) | ICCV 2021 | [[code](https://github.com/kimren227/CSGStumpNet)] |\n| [UV-Net: Learning from Boundary Representations](https://arxiv.org/abs/2006.10211) | CVPR 2021 | [[code](https://github.com/AutodeskAILab/UV-Net)] |\n| [BRepNet: A Topological Message Passing System for Solid Models](https://arxiv.org/abs/2104.00706) | CVPR 2021 | [[code](https://github.com/AutodeskAILab/BRepNet)] |\n| [CSGNet: Neural Shape Parser for Constructive Solid Geometry](https://arxiv.org/abs/1712.08290) | CVPR 2018 | [[code](https://github.com/Hippogriff/CSGNet)] |\n\n## CAD Recognition\n\n| Papers | Venue | Links |\n|--------|-------|-------|\n| [ArchCAD-400K: An Open Large-Scale Architectural CAD Dataset and New Baseline for Panoptic Symbol Spotting](https://arxiv.org/abs/2503.22346) | arXiv 2025 | |\n| [CADSpotting: Robust Panoptic Symbol Spotting on Large-Scale CAD Drawings](https://arxiv.org/abs/2412.07377) | arXiv 2024 | |\n| [Symbol as Points: Panoptic Symbol Spotting via Point-based Representation](https://arxiv.org/abs/2401.10556) | ICLR 2024 | [[code](https://github.com/nicehuster/SymPoint)] |\n| [VectorFloorSeg: Two-Stream Graph Attention Network for Vectorized Roughcast Floorplan Segmentation](https://openaccess.thecvf.com/content/CVPR2023/html/Yang_VectorFloorSeg_Two-Stream_Graph_Attention_Network_for_Vectorized_Roughcast_Floorplan_Segmentation_CVPR_2023_paper.html) | CVPR 2023 | [[code](https://github.com/DrZiji/VecFloorSeg)] |\n| [CADTransformer: Panoptic Symbol Spotting Transformer for CAD Drawings](https://openaccess.thecvf.com/content/CVPR2022/papers/Fan_CADTransformer_Panoptic_Symbol_Spotting_Transformer_for_CAD_Drawings_CVPR_2022_paper.pdf)| CVPR 2022 | [[code](https://github.com/VITA-Group/CADTransformer)] |\n| [GAT-CADNet: Graph Attention Network for Panoptic Symbol Spotting in CAD Drawings](https://arxiv.org/abs/2201.00625) | CVPR 2022 | |\n| [FloorPlanCAD: A Large-Scale CAD Drawing Dataset for Panoptic Symbol Spotting](https://arxiv.org/abs/2105.07147) | ICCV 2021 | [[project](https://floorplancad.github.io/)] |\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbertjiazheng%2FAwesome-CAD","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbertjiazheng%2FAwesome-CAD","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbertjiazheng%2FAwesome-CAD/lists"}