{"type":"ScholarlyArticle","title":"CADParser: A Learning Approach of Sequence Modeling for B-Rep CAD","authors":[{"name":"Shengdi Zhou","slug":"shengdi-zhou","given":"Shengdi","family":"Zhou","affiliation":"State Key Laboratory of Virtual Reality Technology and Systems , Beihang University"},{"name":"Tianyi Tang","slug":"tianyi-tang","given":"Tianyi","family":"Tang","affiliation":"University of Waterloo"},{"name":"Bin Zhou","slug":"bin-zhou","given":"Bin","family":"Zhou","affiliation":"State Key Laboratory of Virtual Reality Technology and Systems , Beihang University"}],"abstract":"Computer-Aided Design (CAD) plays a crucial role in industrial manufacturing by providing geometry information and the construction workflow for manufactured objects. The construction information enables effective re-editing of parametric CAD models. While boundary representation (B-Rep) is the standard format for representing geometry structures, JSON format is an alternative due to the lack of uniform criteria for storing the construction workflow. Regrettably, most CAD models available on the Internet only offer geometry information, omitting the construction procedure and hampering creation efficiency. This paper proposes a learning approach CADParser to infer the underlying modeling sequences given a B-Rep CAD model. It achieves this by treating the CAD geometry structure as a graph and the construction workflow as a sequence. Since the existing CAD dataset only contains two operations (i.e., Sketch and Extrusion), limiting the diversity of the CAD model creation, we also introduce a large-scale dataset incorporating a more comprehensive range of operations such as Revolution, Fillet, and Chamfer. Each model includes both the geometry structure and the construction sequences. Extensive experiments demonstrate that our method can compete with the existing state-of-the-art methods quantitatively and qualitatively. Data is available at https://drive.google.com/CADParserData.","doi":"10.24963/ijcai.2023/200","url":"https://ijcai.org/proceedings/2023/200","pdf":"https://ijcai.org/proceedings/2023/0200.pdf","markdown":"https://ijcai.org/proceedings/2023/200.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/200","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Computer Vision","track":"Main Track","number":200,"pages":{"first":1804,"last":1812},"keywords":["Computer Vision: CV: 3D computer vision","Computer Vision: CV: Applications"],"videos":[],"references":[{"position":1,"raw":"[Chang et al., 2015] Angel X Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, et al. Shapenet: An information-rich 3d model repository. arXiv preprint arXiv:1512.03012, 2015.","title":"Shapenet: An information-rich 3d model repository","authors":"Thomas Angel X Chang, Leonidas Funkhouser, Pat Guibas, Qixing Hanrahan, Zimo Huang, Silvio Li, Manolis Savarese, Shuran Savva, Hao Song, Su","year":"2015","venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Du et al., 2018] Tao Du, Jeevana Priya Inala, Yewen Pu, Andrew Spielberg, Adriana Schulz, Daniela Rus, Ar- mando Solar-Lezama, and Wojciech Matusik. Inversecsg: Automatic conversion of 3d models to csg trees. ACM Transactions on Graphics (TOG), 37(6):1-16, 2018.","title":"Inversecsg: Automatic conversion of 3d models to csg trees","authors":"Tao Du, Jeevana Priya Inala, Yewen Pu, Andrew Spielberg, Adriana Schulz, Daniela Rus, Armando Solar-Lezama, Wojciech Matusik","year":"2018","venue":"ACM Transactions on Graphics (TOG)","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Ganin et al., 2021] Yaroslav Ganin, Sergey Bartunov, Yujia Li, Ethan Keller, and Stefano Saliceti. Computer-aided design as language. Advances in Neural Information Pro- cessing Systems, 34, 2021.","title":"Computer-aided design as language","authors":"Yaroslav Ganin, Sergey Bartunov, Yujia Li, Ethan Keller, Stefano Saliceti","year":"2021","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Hanocka et al., 2019] Rana Hanocka, Amir Hertz, Noa Fish, Raja Giryes, Shachar Fleishman, and Daniel Cohen- Or. Meshcnn: a network with an edge. ACM Transactions on Graphics (TOG), 38(4):1-12, 2019.","title":"Meshcnn: a network with an edge","authors":"Rana Hanocka, Amir Hertz, Noa Fish, Raja Giryes, Shachar Fleishman, Daniel Cohen-Or","year":"2019","venue":"ACM Transactions on Graphics (TOG)","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Jayaraman et al., 2021] Pradeep Kumar Jayaraman, Aditya Sanghi, Joseph G Lambourne, Karl DD Willis, Thomas Davies, Hooman Shayani, and Nigel Morris. Uv-net: Learning from boundary representations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pat- tern Recognition, pages 11703-11712, 2021.","title":"Uv-net: Learning from boundary representations","authors":"Pradeep Kumar Jayaraman, Aditya Sanghi, Karl Dd Joseph G Lambourne, Thomas Willis, Hooman Davies, Nigel Shayani, Morris","year":"2021","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Kania et al., 2020] Kacper Kania, Maciej Zieba, and Tomasz Kajdanowicz. Ucsg-net-unsupervised discovering of constructive solid geometry tree. Advances in Neural Information Processing Systems, 33:8776-8786, 2020.","title":"Ucsg-net-unsupervised discovering of constructive solid geometry tree","authors":"Kacper Kania, Maciej Zieba, Tomasz Kajdanowicz","year":"2020","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Kim et al., 2020] Sangpil Kim, Hyung-gun Chi, Xiao Hu, Qixing Huang, and Karthik Ramani. A large-scale anno- tated mechanical components benchmark for classification and retrieval tasks with deep neural networks. In European Conference on Computer Vision, pages 175-191. Springer, 2020.","title":"A large-scale annotated mechanical components benchmark for classification and retrieval tasks with deep neural networks","authors":"Kim","year":"2020","venue":"European Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Koch et al., 2019] Sebastian Koch, Albert Matveev, Zhong- shi Jiang, Francis Williams, Alexey Artemov, Evgeny Bur- naev, Marc Alexa, Denis Zorin, and Daniele Panozzo. Abc: A big cad model dataset for geometric deep learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9601-9611, 2019.","title":"Abc: A big cad model dataset for geometric deep learning","authors":"Sebastian Koch, Albert Matveev, Zhongshi Jiang, Francis Williams, Alexey Artemov, Evgeny Burnaev, Marc Alexa, Denis Zorin, Daniele Panozzo","year":"2019","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Laidlaw et al., 1986] David H. Laidlaw, W. Benjamin Trumbore, and John F. Hughes. Constructive solid geom- etry for polyhedral objects. In Computer Graphics (Pro- ceedings of SIGGRAPH 86), volume 20, pages 161-170, August 1986.","title":"Constructive solid geometry for polyhedral objects","authors":"H David, W Laidlaw, John F Benjamin Trumbore, Hughes","year":"1986","venue":"Computer Graphics (Proceedings of SIGGRAPH","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Lambourne et al., 2021] Joseph G Lambourne, Karl DD Willis, Pradeep Kumar Jayaraman, Aditya Sanghi, Peter Meltzer, and Hooman Shayani. Brepnet: A topological message passing system for solid models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pat- tern Recognition, pages 12773-12782, 2021.","title":"Brepnet: A topological message passing system for solid models","authors":"Karl Dd Joseph G Lambourne, Pradeep Willis, Aditya Kumar Jayaraman, Peter Sanghi, Hooman Meltzer, Shayani","year":"2021","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Li et al., 2020] Changjian Li, Hao Pan, Adrien Bousseau, and Niloy J Mitra. Sketch2cad: Sequential cad modeling by sketching in context. ACM Transactions on Graphics (TOG), 39(6):1-14, 2020.","title":"Sketch2cad: Sequential cad modeling by sketching in context","authors":"Li","year":"2020","venue":"ACM Transactions on Graphics (TOG)","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Loshchilov and Hutter, 2017] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017.","title":"Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization","authors":"Hutter Loshchilov","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Mescheder et al., 2019] Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger. Occupancy networks: Learning 3d reconstruc- tion in function space. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4460-4470, 2019.","title":"Occupancy networks: Learning 3d reconstruction in function space","authors":"Mescheder","year":"2019","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Mo et al., 2019] Kaichun Mo, Shilin Zhu, Angel X Chang, Li Yi, Subarna Tripathi, Leonidas J Guibas, and Hao Su. Partnet: A large-scale benchmark for fine-grained and hi- erarchical part-level 3d object understanding. In Proceed- ings of the IEEE/CVF conference on computer vision and pattern recognition, pages 909-918, 2019.","title":"Partnet: A large-scale benchmark for fine-grained and hierarchical part-level 3d object understanding","authors":"Kaichun Mo, Shilin Zhu, X Angel, Li Chang, Subarna Yi, Leonidas J Tripathi, Hao Guibas, Su","year":"2019","venue":"Proceedings of the IEEE/CVF conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Nash et al., 2020] Charlie Nash, Yaroslav Ganin, SM Ali Eslami, and Peter Battaglia. Polygen: An autoregressive generative model of 3d meshes. In International Con- ference on Machine Learning, pages 7220-7229. PMLR, 2020. [Ons, ] Onshape. https://www.onshape.com/, Accessed: 2019-08-01.","title":"Polygen: An autoregressive generative model of 3d meshes","authors":"Charlie Nash, Yaroslav Ganin, Ali Eslami, Peter Battaglia","year":"2019","venue":"International Conference on Machine Learning","doi":null,"url":"https://www.onshape.com/","internal_url":null},{"position":16,"raw":"[Para et al., 2021] Wamiq Para, Shariq Bhat, Paul Guerrero, Tom Kelly, Niloy Mitra, Leonidas J Guibas, and Peter Wonka. Sketchgen: Generating constrained cad sketches. Advances in Neural Information Processing Systems, 34, 2021.","title":"Sketchgen: Generating constrained cad sketches","authors":"Para","year":"2021","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Pezoa et al., 2016] Felipe Pezoa, Juan L Reutter, Fernando Suarez, Martín Ugarte, and Domagoj Vrgoč. Foundations of json schema. In Proceedings of the 25th International Conference on World Wide Web, pages 263-273, 2016.","title":"Foundations of json schema","authors":"Felipe Pezoa, Juan L Reutter, Fernando Suarez, Martín Ugarte, Domagoj Vrgoč","year":"2016","venue":"Proceedings of the 25th International Conference on World Wide Web","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Qi et al., 2017] Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas. Pointnet: Deep learning on point sets for 3d classification and segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 652-660, 2017.","title":"Pointnet: Deep learning on point sets for 3d classification and segmentation","authors":"Hao Charles R Qi, Kaichun Su, Leonidas J Mo, Guibas","year":"2017","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Seff et al., 2020] Ari Seff, Yaniv Ovadia, Wenda Zhou, and Ryan P. Adams. SketchGraphs: A large-scale dataset for modeling relational geometry in computer-aided design. In ICML 2020 Workshop on Object-Oriented Learning, 2020.","title":"SketchGraphs: A large-scale dataset for modeling relational geometry in computer-aided design","authors":"Ari Seff, Yaniv Ovadia, Wenda Zhou, Ryan P Adams","year":"2020","venue":"ICML 2020 Workshop on Object-Oriented Learning","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Sharma et al., 2018] Gopal Sharma, Rishabh Goyal, Difan Liu, Evangelos Kalogerakis, and Subhransu Maji. Csgnet: Neural shape parser for constructive solid geometry. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 5515-5523, 2018.","title":"Csgnet: Neural shape parser for constructive solid geometry","authors":"Gopal Sharma, Rishabh Goyal, Difan Liu, Evangelos Kalogerakis, Subhransu Maji","year":"2018","venue":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Vaswani et al., 2017] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017.","title":"Attention is all you need","authors":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, Illia Polosukhin","year":"2017","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Wang et al., 2021] Xinpeng Wang, Chandan Yeshwanth, and Matthias Nießner. Sceneformer: Indoor scene genera- tion with transformers. In 2021 International Conference on 3D Vision (3DV), pages 106-115. IEEE, 2021.","title":"Sceneformer: Indoor scene generation with transformers","authors":"Wang","year":"2021","venue":"International Conference on 3D Vision (3DV)","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Willis et al., 2021a] Karl DD Willis, Pradeep Kumar Ja- yaraman, Joseph G Lambourne, Hang Chu, and Yewen Pu. Engineering sketch generation for computer-aided design. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2105-2114, 2021.","title":"Engineering sketch generation for computer-aided design","authors":"Willis","year":"2021","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Willis et al., 2021b] Karl DD Willis, Yewen Pu, Jieliang Luo, Hang Chu, Tao Du, Joseph G Lambourne, Armando Solar-Lezama, and Wojciech Matusik. Fusion 360 gallery: A dataset and environment for programmatic cad construc- tion from human design sequences. ACM Transactions on Graphics (TOG), 40(4):1-24, 2021.","title":"Fusion 360 gallery: A dataset and environment for programmatic cad construction from human design sequences","authors":"Willis","year":"2021","venue":"ACM Transactions on Graphics (TOG)","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Wu et al., 2015] Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, and Jianxiong Xiao. 3d shapenets: A deep representation for volumetric shapes. In Proceedings of the IEEE conference on com- puter vision and pattern recognition, pages 1912-1920, 2015.","title":"3d shapenets: A deep representation for volumetric shapes","authors":"Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, Jianxiong Xiao","year":"2015","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Wu et al., 2018] Qiaoyun Wu, Kai Xu, and Jun Wang. Con- structing 3d csg models from 3d raw point clouds. In Com- puter Graphics Forum, volume 37, pages 221-232. Wiley Online Library, 2018.","title":"Constructing 3d csg models from 3d raw point clouds","authors":"Wu","year":"2018","venue":"Computer Graphics Forum","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Wu et al., 2021] Rundi Wu, Chang Xiao, and Changxi Zheng. Deepcad: A deep generative network for computer-aided design models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6772-6782, 2021.","title":"Deepcad: A deep generative network for computer-aided design models","authors":"Rundi Wu, Chang Xiao, Changxi Zheng","year":"2021","venue":"Proceedings of the IEEE/CVF International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Xu et al., 2021] Xianghao Xu, Wenzhe Peng, Chin-Yi Cheng, Karl DD Willis, and Daniel Ritchie. Inferring cad modeling sequences using zone graphs. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pat- tern Recognition, pages 6062-6070, 2021.","title":"Inferring cad modeling sequences using zone graphs","authors":"Xu","year":"2021","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}