{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T08:36:14Z","timestamp":1774600574996,"version":"3.50.1"},"reference-count":44,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2021,7,19]],"date-time":"2021-07-19T00:00:00Z","timestamp":1626652800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Science Foundation","award":["1941808"],"award-info":[{"award-number":["1941808"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Graph."],"published-print":{"date-parts":[[2021,8,31]]},"abstract":"<jats:p>\n            A popular way to create detailed yet easily controllable 3D shapes is via procedural modeling, i.e. generating geometry using programs. Such programs consist of a series of instructions along with their associated parameter values. To fully realize the benefits of this representation, a shape program should be compact and only expose degrees of freedom that allow for meaningful manipulation of output geometry. One way to achieve this goal is to design higher-level\n            <jats:italic>macro<\/jats:italic>\n            operators that, when executed, expand into a series of commands from the base shape modeling language. However, manually authoring such macros, much like shape programs themselves, is difficult and largely restricted to domain experts. In this paper, we present ShapeMOD, an algorithm for automatically discovering macros that are useful across large datasets of 3D shape programs. ShapeMOD operates on shape programs expressed in an imperative, statement-based language. It is designed to discover macros that make programs more compact by minimizing the number of function calls and free parameters required to represent an input shape collection. We run ShapeMOD on multiple collections of programs expressed in a domain-specific language for 3D shape structures. We show that it automatically discovers a concise set of macros that abstract out common structural and parametric patterns that generalize over large shape collections. We also demonstrate that the macros found by ShapeMOD improve performance on downstream tasks including shape generative modeling and inferring programs from point clouds. Finally, we conduct a user study that indicates that ShapeMOD's discovered macros make interactive shape editing more efficient.\n          <\/jats:p>","DOI":"10.1145\/3450626.3459821","type":"journal-article","created":{"date-parts":[[2021,7,20]],"date-time":"2021-07-20T00:04:27Z","timestamp":1626739467000},"page":"1-16","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":24,"title":["ShapeMOD"],"prefix":"10.1145","volume":"40","author":[{"given":"R. Kenny","family":"Jones","sequence":"first","affiliation":[{"name":"Brown University"}]},{"given":"David","family":"Charatan","sequence":"additional","affiliation":[{"name":"Brown University"}]},{"given":"Paul","family":"Guerrero","sequence":"additional","affiliation":[{"name":"Adobe Research"}]},{"given":"Niloy J.","family":"Mitra","sequence":"additional","affiliation":[{"name":"University College London and Adobe Research"}]},{"given":"Daniel","family":"Ritchie","sequence":"additional","affiliation":[{"name":"Brown University"}]}],"member":"320","published-online":{"date-parts":[[2021,7,19]]},"reference":[{"key":"e_1_2_2_1_1","volume-title":"Proceedings of the 35th International Conference on Machine Learning (Proceedings of Machine Learning Research","volume":"49","author":"Achlioptas Panos","year":"2018","unstructured":"Panos Achlioptas , Olga Diamanti , Ioannis Mitliagkas , and Leonidas Guibas . 2018 . Learning Representations and Generative Models for 3D Point Clouds . In Proceedings of the 35th International Conference on Machine Learning (Proceedings of Machine Learning Research , Vol. 80), Jennifer Dy and Andreas Krause (Eds.). PMLR, Stockholmsm\u00e4ssan, Stockholm Sweden, 40-- 49 . http:\/\/proceedings.mlr.press\/v80\/achlioptas18a.html Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, and Leonidas Guibas. 2018. Learning Representations and Generative Models for 3D Point Clouds. In Proceedings of the 35th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 80), Jennifer Dy and Andreas Krause (Eds.). PMLR, Stockholmsm\u00e4ssan, Stockholm Sweden, 40--49. http:\/\/proceedings.mlr.press\/v80\/achlioptas18a.html"},{"key":"e_1_2_2_2_1","volume-title":"ShapeNet: An Information-Rich 3D Model Repository. arXiv:1512.03012","author":"Chang Angel X.","year":"2015","unstructured":"Angel X. Chang , Thomas Funkhouser , Leonidas Guibas , Pat Hanrahan , Qixing Huang , Zimo Li , Silvio Savarese , Manolis Savva , Shuran Song , Hao Su , Jianxiong Xiao , Li Yi , and Fisher Yu. 2015. ShapeNet: An Information-Rich 3D Model Repository. arXiv:1512.03012 ( 2015 ). Angel X. Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, Jianxiong Xiao, Li Yi, and Fisher Yu. 2015. ShapeNet: An Information-Rich 3D Model Repository. arXiv:1512.03012 (2015)."},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.14020"},{"key":"e_1_2_2_4_1","volume-title":"Learning Implicit Fields for Generative Shape Modeling. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR).","author":"Chen Zhiqin","year":"2019","unstructured":"Zhiqin Chen and Hao Zhang . 2019 . Learning Implicit Fields for Generative Shape Modeling. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Zhiqin Chen and Hao Zhang. 2019. Learning Implicit Fields for Generative Shape Modeling. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR)."},{"key":"e_1_2_2_5_1","volume-title":"Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence","author":"Dechter Eyal","unstructured":"Eyal Dechter , Jon Malmaud , Ryan P. Adams , and Joshua B. Tenenbaum . 2013. Bootstrap Learning via Modular Concept Discovery . In Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence ( Beijing, China) (IJCAI '13). AAAI Press, 1302--1309. Eyal Dechter, Jon Malmaud, Ryan P. Adams, and Joshua B. Tenenbaum. 2013. Bootstrap Learning via Modular Concept Discovery. In Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence (Beijing, China) (IJCAI '13). AAAI Press, 1302--1309."},{"key":"e_1_2_2_6_1","volume-title":"CvxNet: Learnable Convex Decomposition. (June","author":"Deng Boyang","year":"2020","unstructured":"Boyang Deng , Kyle Genova , Soroosh Yazdani , Sofien Bouaziz , Geoffrey Hinton , and Andrea Tagliasacchi . 2020. CvxNet: Learnable Convex Decomposition. (June 2020 ). Boyang Deng, Kyle Genova, Soroosh Yazdani, Sofien Bouaziz, Geoffrey Hinton, and Andrea Tagliasacchi. 2020. CvxNet: Learnable Convex Decomposition. (June 2020)."},{"key":"e_1_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3272127.3275006"},{"key":"e_1_2_2_8_1","volume-title":"Tenenbaum","author":"Ellis Kevin","year":"2018","unstructured":"Kevin Ellis , Lucas Morales , Mathias Sabl\u00e9-Meyer , Armando Solar-Lezama , and Joshua B . Tenenbaum . 2018 a. Library Learning for Neurally-Guided Bayesian Program Induction . In Advances in Neural Information Processing Systems (NeurIPS). Kevin Ellis, Lucas Morales, Mathias Sabl\u00e9-Meyer, Armando Solar-Lezama, and Joshua B. Tenenbaum. 2018a. Library Learning for Neurally-Guided Bayesian Program Induction. In Advances in Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_2_2_9_1","volume-title":"Write","author":"Ellis Kevin","unstructured":"Kevin Ellis , Maxwell Nye , Yewen Pu , Felix Sosa , Josh Tenenbaum , and Armando Solar-Lezama . 2019. Write , Execute, Assess : Program Synthesis with a REPL. In Advances in Neural Information Processing Systems (NeurIPS) . Kevin Ellis, Maxwell Nye, Yewen Pu, Felix Sosa, Josh Tenenbaum, and Armando Solar-Lezama. 2019. Write, Execute, Assess: Program Synthesis with a REPL. In Advances in Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_2_2_10_1","unstructured":"Kevin Ellis Daniel Ritchie Armando Solar-Lezama and Josh Tenenbaum. 2018b. Learning to Infer Graphics Programs from Hand-Drawn Images. In Advances in Neural Information Processing Systems (NeurIPS).  Kevin Ellis Daniel Ritchie Armando Solar-Lezama and Josh Tenenbaum. 2018b. Learning to Infer Graphics Programs from Hand-Drawn Images. In Advances in Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_2_2_11_1","volume-title":"Tenenbaum","author":"Ellis Kevin","year":"2020","unstructured":"Kevin Ellis , Catherine Wong , Maxwell Nye , Mathias Sable-Meyer , Luc Cary , Lucas Morales , Luke Hewitt , Armando Solar-Lezama , and Joshua B . Tenenbaum . 2020 . DreamCoder : Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning. arXiv:2006.08381 [cs.AI] Kevin Ellis, Catherine Wong, Maxwell Nye, Mathias Sable-Meyer, Luc Cary, Lucas Morales, Luke Hewitt, Armando Solar-Lezama, and Joshua B. Tenenbaum. 2020. DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning. arXiv:2006.08381 [cs.AI]"},{"key":"e_1_2_2_12_1","doi-asserted-by":"crossref","unstructured":"Lin Gao Jie Yang Tong Wu Yu-Jie Yuan Hongbo Fu Yu-Kun Lai and Hao (Richard) Zhang. 2019. SDM-NET: Deep Generative Network for Structured Deformable Mesh. In SIGGRAPH Asia.  Lin Gao Jie Yang Tong Wu Yu-Jie Yuan Hongbo Fu Yu-Kun Lai and Hao (Richard) Zhang. 2019. SDM-NET: Deep Generative Network for Structured Deformable Mesh. In SIGGRAPH Asia.","DOI":"10.1145\/3355089.3356488"},{"key":"e_1_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00725"},{"key":"e_1_2_2_14_1","volume-title":"IEEE Conference on Computer Vision and Pattern Recognition (CVPR).","author":"Groueix Thibault","year":"2018","unstructured":"Thibault Groueix , Matthew Fisher , Vladimir G. Kim , Bryan C. Russell , and Mathieu Aubry . 2018 . AtlasNet: A Papier-M\u00e2ch\u00e9 Approach to Learning 3D Surface Generation . In IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Thibault Groueix, Matthew Fisher, Vladimir G. Kim, Bryan C. Russell, and Mathieu Aubry. 2018. AtlasNet: A Papier-M\u00e2ch\u00e9 Approach to Learning 3D Surface Generation. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR)."},{"key":"e_1_2_2_15_1","unstructured":"Martin Heusel Hubert Ramsauer Thomas Unterthiner Bernhard Nessler and Sepp Hochreiter. 2017. GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium. In NeurIPS.  Martin Heusel Hubert Ramsauer Thomas Unterthiner Bernhard Nessler and Sepp Hochreiter. 2017. GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium. In NeurIPS."},{"key":"e_1_2_2_16_1","volume-title":"Goodman","author":"Hwang Irvin","year":"2011","unstructured":"Irvin Hwang , Andreas Stuhlm\u00fcller , and Noah D . Goodman . 2011 . Inducing Probabilistic Programs by Bayesian Program Merging. CoRR arXiv:1110.5667 (2011). Irvin Hwang, Andreas Stuhlm\u00fcller, and Noah D. Goodman. 2011. Inducing Probabilistic Programs by Bayesian Program Merging. CoRR arXiv:1110.5667 (2011)."},{"key":"e_1_2_2_17_1","volume-title":"Siggraph Asia 2020 39, 6","author":"Jones R. Kenny","year":"2020","unstructured":"R. Kenny Jones , Theresa Barton , Xianghao Xu , Kai Wang , Ellen Jiang , Paul Guerrero , Niloy J. Mitra , and Daniel Ritchie . 2020. ShapeAssembly: Learning to Generate Programs for 3D Shape Structure Synthesis. ACM Transactions on Graphics (TOG) , Siggraph Asia 2020 39, 6 ( 2020 ), Article 234. R. Kenny Jones, Theresa Barton, Xianghao Xu, Kai Wang, Ellen Jiang, Paul Guerrero, Niloy J. Mitra, and Daniel Ritchie. 2020. ShapeAssembly: Learning to Generate Programs for 3D Shape Structure Synthesis. ACM Transactions on Graphics (TOG), Siggraph Asia 2020 39, 6 (2020), Article 234."},{"key":"e_1_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3072959.3073599"},{"key":"e_1_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/1964921.1964980"},{"key":"e_1_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3072959.3073637"},{"key":"e_1_2_2_21_1","volume-title":"Neurally-Guided Structure Inference. In International Conference on Machine Learning (ICML).","author":"Lu Sidi","year":"2019","unstructured":"Sidi Lu , Jiayuan Mao , Joshua B. Tenenbaum , and Jiajun Wu . 2019 . Neurally-Guided Structure Inference. In International Conference on Machine Learning (ICML). Sidi Lu, Jiayuan Mao, Joshua B. Tenenbaum, and Jiajun Wu. 2019. Neurally-Guided Structure Inference. In International Conference on Machine Learning (ICML)."},{"key":"e_1_2_2_22_1","doi-asserted-by":"crossref","unstructured":"A. Martinovic and L. Van Gool. 2013. Bayesian Grammar Learning for Inverse Procedural Modeling. In CVPR.  A. Martinovic and L. Van Gool. 2013. Bayesian Grammar Learning for Inverse Procedural Modeling. In CVPR.","DOI":"10.1109\/CVPR.2013.33"},{"key":"e_1_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/1618452.1618483"},{"key":"e_1_2_2_24_1","doi-asserted-by":"crossref","unstructured":"Niloy Mitra Michael Wand Hao (Richard) Zhang Daniel Cohen-Or Vladimir Kim and Qi-Xing Huang. 2013. Structure-Aware Shape Processing. In SIGGRAPH Asia 2013 Courses. Article 1 20 pages.  Niloy Mitra Michael Wand Hao (Richard) Zhang Daniel Cohen-Or Vladimir Kim and Qi-Xing Huang. 2013. Structure-Aware Shape Processing. In SIGGRAPH Asia 2013 Courses. Article 1 20 pages.","DOI":"10.1145\/2542266.2542267"},{"key":"e_1_2_2_25_1","unstructured":"Kaichun Mo Paul Guerrero Li Yi Hao Su Peter Wonka Niloy Mitra and Leonidas Guibas. 2019a. StructureNet: Hierarchical Graph Networks for 3D Shape Generation. In SIGGRAPH Asia.  Kaichun Mo Paul Guerrero Li Yi Hao Su Peter Wonka Niloy Mitra and Leonidas Guibas. 2019a. StructureNet: Hierarchical Graph Networks for 3D Shape Generation. In SIGGRAPH Asia."},{"key":"e_1_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00100"},{"key":"e_1_2_2_27_1","doi-asserted-by":"crossref","unstructured":"Pascal M\u00fcller Peter Wonka Simon Haegler Andreas Ulmer and Luc Van Gool. 2006. Procedural Modeling of Buildings. In SIGGRAPH.  Pascal M\u00fcller Peter Wonka Simon Haegler Andreas Ulmer and Luc Van Gool. 2006. Procedural Modeling of Buildings. In SIGGRAPH.","DOI":"10.1145\/1179352.1141931"},{"key":"e_1_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3385412.3386012"},{"key":"e_1_2_2_29_1","volume-title":"Parish and Pascal M\u00fcller","author":"Yoav I.","year":"2001","unstructured":"Yoav I. H. Parish and Pascal M\u00fcller . 2001 . Procedural Modeling of Cities. In SIGGRAPH. Przemyslaw Prusinkiewicz and Aristid Lindenmayer. 1996. The Algorithmic Beauty of Plants. Springer-Verlag , Berlin, Heidelberg. Yoav I. H. Parish and Pascal M\u00fcller. 2001. Procedural Modeling of Cities. In SIGGRAPH. Przemyslaw Prusinkiewicz and Aristid Lindenmayer. 1996. The Algorithmic Beauty of Plants. Springer-Verlag, Berlin, Heidelberg."},{"key":"e_1_2_2_30_1","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 652--660","author":"Qi Charles R","year":"2017","unstructured":"Charles R Qi , Hao Su , Kaichun Mo , and Leonidas J Guibas . 2017 . Pointnet: Deep learning on point sets for 3D classification and segmentation . In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 652--660 . Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas. 2017. Pointnet: Deep learning on point sets for 3D classification and segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 652--660."},{"key":"e_1_2_2_31_1","doi-asserted-by":"crossref","unstructured":"Daniel Ritchie Sarah Jobalia and Anna Thomas. 2018. Example-based Authoring of Procedural Modeling Programs with Structural and Continuous Variability. In EUROGRAPHICS.  Daniel Ritchie Sarah Jobalia and Anna Thomas. 2018. Example-based Authoring of Procedural Modeling Programs with Structural and Continuous Variability. In EUROGRAPHICS.","DOI":"10.1111\/cgf.13371"},{"key":"e_1_2_2_32_1","volume-title":"CSGNet: Neural Shape Parser for Constructive Solid Geometry. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR).","author":"Sharma Gopal","year":"2018","unstructured":"Gopal Sharma , Rishabh Goyal , Difan Liu , Evangelos Kalogerakis , and Subhransu Maji . 2018 . CSGNet: Neural Shape Parser for Constructive Solid Geometry. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Gopal Sharma, Rishabh Goyal, Difan Liu, Evangelos Kalogerakis, and Subhransu Maji. 2018. CSGNet: Neural Shape Parser for Constructive Solid Geometry. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR)."},{"key":"e_1_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3355089.3356529"},{"key":"e_1_2_2_34_1","doi-asserted-by":"crossref","unstructured":"Jerry O. Talton Lingfeng Yang Ranjitha Kumar Maxine Lim Noah D. Goodman and Radom\u00edr Mech. 2012. Learning design patterns with Bayesian grammar induction. In UIST.  Jerry O. Talton Lingfeng Yang Ranjitha Kumar Maxine Lim Noah D. Goodman and Radom\u00edr Mech. 2012. Learning design patterns with Bayesian grammar induction. In UIST.","DOI":"10.1145\/2380116.2380127"},{"key":"e_1_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/1480881.1480915"},{"key":"e_1_2_2_36_1","volume-title":"International Conference on Learning Representations (ICLR).","author":"Tian Yonglong","year":"2019","unstructured":"Yonglong Tian , Andrew Luo , Xingyuan Sun , Kevin Ellis , William T. Freeman , Joshua B. Tenenbaum , and Jiajun Wu . 2019 . Learning to Infer and Execute 3D Shape Programs . In International Conference on Learning Representations (ICLR). Yonglong Tian, Andrew Luo, Xingyuan Sun, Kevin Ellis, William T. Freeman, Joshua B. Tenenbaum, and Jiajun Wu. 2019. Learning to Infer and Execute 3D Shape Programs. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.160"},{"key":"e_1_2_2_38_1","unstructured":"Homer Walke R. Kenny Jones and Daniel Ritchie. 2020. Learning to Infer Shape Programs Using Latent Execution Self Training. arXiv:2011.13045 [cs.CV]  Homer Walke R. Kenny Jones and Daniel Ritchie. 2020. Learning to Infer Shape Programs Using Latent Execution Self Training. arXiv:2011.13045 [cs.CV]"},{"key":"e_1_2_2_39_1","volume-title":"Fusion 360 Gallery: A Dataset and Environment for Programmatic CAD Reconstruction. arXiv preprint arXiv:2010.02392","author":"Willis Karl D. D.","year":"2020","unstructured":"Karl D. D. Willis , Yewen Pu , Jieliang Luo , Hang Chu , Tao Du , Joseph G. Lambourne , Armando Solar-Lezama , and Wojciech Matusik . 2020. Fusion 360 Gallery: A Dataset and Environment for Programmatic CAD Reconstruction. arXiv preprint arXiv:2010.02392 ( 2020 ). Karl D. D. Willis, Yewen Pu, Jieliang Luo, Hang Chu, Tao Du, Joseph G. Lambourne, Armando Solar-Lezama, and Wojciech Matusik. 2020. Fusion 360 Gallery: A Dataset and Environment for Programmatic CAD Reconstruction. arXiv preprint arXiv:2010.02392 (2020)."},{"key":"e_1_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3355089.3356518"},{"key":"e_1_2_2_41_1","volume-title":"Tenenbaum","author":"Wu Jiajun","year":"2016","unstructured":"Jiajun Wu , Chengkai Zhang , Tianfan Xue , William T. Freeman , and Joshua B . Tenenbaum . 2016 . Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling. In Advances in Neural Information Processing Systems (NeurIPS) . Jiajun Wu, Chengkai Zhang, Tianfan Xue, William T. Freeman, and Joshua B. Tenenbaum. 2016. Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling. In Advances in Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_2_2_42_1","unstructured":"Jie Yang Kaichun Mo Yu-Kun Lai Leonidas J. Guibas and Lin Gao. 2020. DSM-Net: Disentangled Structured Mesh Net for Controllable Generation of Fine Geometry. arXiv:2008.05440 [cs.GR]  Jie Yang Kaichun Mo Yu-Kun Lai Leonidas J. Guibas and Lin Gao. 2020. DSM-Net: Disentangled Structured Mesh Net for Controllable Generation of Fine Geometry. arXiv:2008.05440 [cs.GR]"},{"key":"e_1_2_2_43_1","volume-title":"Co-Abstraction of Shape Collections. ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia 2012) 31","author":"Yumer Mehmet Ersin","year":"2012","unstructured":"Mehmet Ersin Yumer and Levent Burak Kara . 2012. Co-Abstraction of Shape Collections. ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia 2012) 31 ( 2012 ), 166:1--166:11. Issue 6. Mehmet Ersin Yumer and Levent Burak Kara. 2012. Co-Abstraction of Shape Collections. ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia 2012) 31 (2012), 166:1--166:11. Issue 6."},{"key":"e_1_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.103"}],"container-title":["ACM Transactions on Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3450626.3459821","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3450626.3459821","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3450626.3459821","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:17:20Z","timestamp":1750191440000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3450626.3459821"}},"subtitle":["macro operation discovery for 3D shape programs"],"short-title":[],"issued":{"date-parts":[[2021,7,19]]},"references-count":44,"aliases":["10.1145\/3476576.3476734"],"journal-issue":{"issue":"4","published-print":{"date-parts":[[2021,8,31]]}},"alternative-id":["10.1145\/3450626.3459821"],"URL":"https:\/\/doi.org\/10.1145\/3450626.3459821","relation":{},"ISSN":["0730-0301","1557-7368"],"issn-type":[{"value":"0730-0301","type":"print"},{"value":"1557-7368","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,19]]},"assertion":[{"value":"2021-07-19","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}