A Novel Shape Retrieval Method for 3D Mechanical Components Based on Object Projection, Pre-Trained Deep Learning Models and Autoencoder
The reuse of existing design models offers great potential in saving resources and generating an efficient workflow. In order to fully benefit from these advantages, it is necessary to develop methods that are able to retrieve mechanical engineering geometry from a query input. This paper aims to ad...
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| Published in | Computer aided design Vol. 154; p. 103417 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.01.2023
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0010-4485 1879-2685 |
| DOI | 10.1016/j.cad.2022.103417 |
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| Abstract | The reuse of existing design models offers great potential in saving resources and generating an efficient workflow. In order to fully benefit from these advantages, it is necessary to develop methods that are able to retrieve mechanical engineering geometry from a query input. This paper aims to address this problem by presenting a method that focuses on the needs of product development to retrieve similar components by comparing the geometrical similarity of existing parts. Therefore, a method is described, which first converts surface meshes into point clouds, rotates them, and then transforms the results into matrices. These are subsequently passed to a pre-trained Deep Learning network to extract the feature vector. A similarity between different geometries is calculated and evaluated based on this vector. The procedure employs a new type of part alignment, especially developed for mechanical engineering geometries. The method is presented in detail and several parameters affecting the accuracy of the retrieval are discussed. This is followed by a critical comparison with other shape retrieval approaches through a mechanical engineering benchmark data set.
[Display omitted]
•Method for retrieval of mechanical engineering components.•Transformation of geometries with the projection method into matrices.•Application of pre-trained Deep Learning networks to generate the feature vector.•Improving retrieval results through new alignment method.•Comparison of the new procedure with state of the art methods. |
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| AbstractList | The reuse of existing design models offers great potential in saving resources and generating an efficient workflow. In order to fully benefit from these advantages, it is necessary to develop methods that are able to retrieve mechanical engineering geometry from a query input. This paper aims to address this problem by presenting a method that focuses on the needs of product development to retrieve similar components by comparing the geometrical similarity of existing parts. Therefore, a method is described, which first converts surface meshes into point clouds, rotates them, and then transforms the results into matrices. These are subsequently passed to a pre-trained Deep Learning network to extract the feature vector. A similarity between different geometries is calculated and evaluated based on this vector. The procedure employs a new type of part alignment, especially developed for mechanical engineering geometries. The method is presented in detail and several parameters affecting the accuracy of the retrieval are discussed. This is followed by a critical comparison with other shape retrieval approaches through a mechanical engineering benchmark data set.
[Display omitted]
•Method for retrieval of mechanical engineering components.•Transformation of geometries with the projection method into matrices.•Application of pre-trained Deep Learning networks to generate the feature vector.•Improving retrieval results through new alignment method.•Comparison of the new procedure with state of the art methods. |
| ArticleNumber | 103417 |
| Author | Schleich, B. Bickel, S. Wartzack, S. |
| Author_xml | – sequence: 1 givenname: S. orcidid: 0000-0002-9558-3506 surname: Bickel fullname: Bickel, S. email: bickel@mfk.fau.de – sequence: 2 givenname: B. surname: Schleich fullname: Schleich, B. – sequence: 3 givenname: S. surname: Wartzack fullname: Wartzack, S. |
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| Cites_doi | 10.1145/1060244.1060275 10.1016/j.procir.2016.04.173 10.1007/978-0-387-78414-4_1 10.1109/ICCV.2015.114 10.1007/s00371-008-0304-2 10.1109/5.726791 10.1016/j.procir.2021.01.065 10.1145/3095140.3095147 10.3722/cadaps.2013.789-802 10.1504/IJCAT.2005.006466 10.1016/j.cad.2005.02.011 10.1016/S0031-2023(97)00122-2 10.1109/CVPR.2015.7298845 10.1007/s11042-011-0873-3 10.1007/s11042-013-1850-9 10.1016/j.patrec.2011.01.003 10.1016/j.cad.2004.07.002 10.1007/s11263-010-0395-x 10.1007/s11704-015-4291-y 10.1007/s41095-020-0174-8 10.1007/s11042-007-0181-0 10.1109/CVPR.2018.00716 10.1109/TPAMI.2015.2424863 10.1016/j.cviu.2014.10.006 10.1016/j.cviu.2013.11.008 10.1016/j.cad.2006.06.007 10.1007/s00371-014-0935-4 10.1145/304012.304020 10.1007/s10669-019-09732-4 10.1109/CVPR.2016.308 10.1115/1.1577356 10.1016/j.cad.2019.03.005 10.1145/2377576.2377584 10.1145/1118890.1118893 10.1109/PROC.1984.13073 10.1109/TIP.2014.2336542 10.1016/j.patcog.2006.06.006 10.1145/3065386 10.1007/s00371-013-0819-z 10.1155/2017/6049750 10.1016/j.compind.2020.103320 10.1109/CVPR.2015.7298594 10.1007/s11263-009-0281-6 10.1016/S0010-4485(01)00178-6 10.1016/j.neucom.2015.08.127 10.1109/CVPR.2017.243 10.1109/CVPR.2018.00907 10.1007/s11042-012-1130-0 10.1007/s11042-020-10283-z 10.1145/571647.571648 |
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| Keywords | Projection method Part alignment Shape retrieval 3D object retrieval Autoencoder Deep Learning |
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| References | Yavartanoo, Kim, Lee (b44) 2018 Iyer, Jayanti, Lou, Kalyanaraman, Ramani (b4) 2005; 37 Székely, Rizzo, Bakirov (b52) 2007 Pakkanen, Huhtala, Juuti, Lehtonen (b1) 2016; 50 Lecun, Bottou, Bengio, Haffner (b36) 1998; 86 Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, p. 2818–2826. Li, Johan (b79) 2013; 62 Su H, Maji S, Kalogerakis E, Learned-Miller E. Multi-view convo-lutional neural networks for 3d shape recognition. In: Proceedings of the IEEE international conference on computer vision, 2015, p. 945–53. Horn (b25) 1984; 72 Shih, Lee, Hou, Yeh (b75) 2017; 15 Getto R, Kuijper A, Fellner DW. Unsupervised 3D object retrieval with parameter-free hierarchical clustering. In: Proceedings of the computer graphics international conference, 2017, p. 1–6. Li, Lu, Godil, Schreck, Bustos, Ferreira, Furuya, Fonseca, Johan, Matsuda (b14) 2014; 119 Bickel, Sauer, Schleich, Wartzack (b43) 2021; 96 Bae, Park (b84) 2013; 29 Singh, Yadav, Rana (b50) 2013; 67 Aono, Koyanagi, Tatsuma (b70) 2013 Qi, Su, Mo, Guibas (b42) 2016 Ankerst, Kastenmüller, Kriegel, Seidl (b24) 1999 McWherter, Peabody, Regli, Shokoufandeh (b11) 2001 Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. 2017. Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, p. 4700–8. Iandola, Han, Moskewicz, Ashraf, Dally, Keutzer (b60) 2016 Tangelder, Veltkamp (b21) 2008; 39 Godil (b57) 2020 Zhuang, Zhang, Hou, Zuo, Liu (b72) 2017; 2017 Cardone, Gupta, Karnik (b3) 2003; 3 Sfikas, Theoharis, Pratikakis (b73) 2011; 91 Zhu, Wang, Bai, Yao, Bai (b78) 2016; 204 Li, Zhang, Bai, Shao (b32) 2013 Liu (b33) 2012 Papadakis, Pratikakis, Theoharis, Perantonis (b34) 2010; 89 Stenholm, Corin Stig, Ivansen, Bergsjö (b2) 2019; 39 (b19) 2022 (b20) 2022 Chen, Tian, Shen, Ouhyoung (b29) 2003 Fang Y, Xie J, Dai G, Wang M, Zhu F, Xu T, Wong E. 3d deep shape descriptor. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, p. 2319–28. Vranic (b83) 2005 Jagadeesan P, Wenzel J, Corney JR, Yan XT, Sherlock A, Torres-Sanchez C, Regli W. Validation of purdue engineer-ing shape benchmark clusters by crowdsourcing. In: Proceedings of the International Conference on Product Lifecycle Management, Bath, UK, 2009. Hou, Zhang, Liu (b85) 2007 Cicirello VA, Regli WC. Resolving non-uniqueness in design feature histories. In: Proceedings of the fifth ACM symposium on solid modeling and applications, 1999, p. 76–84. Lian, Godil, Sun (b30) 2010 (b7) 2022 Deng, Dong, Socher, Li, Li, Fei-Fei (b49) 2009 Spruegel, Bickel, Schleich, Wartzack (b45) 2021; 8 Vajna, Weber, Zeman, Hehenberger, Gerhard, Wartzack (b8) 2018 (b18) 2022 Loncaric (b9) 1998; 31 Chaudhari, Bilionis, Panchal (b6) 2019 Bustos, Keim, Saupe, Schreck, Vranić (b15) 2005; 37 Tatsuma, Aono (b66) 2009; 25 Vranic, Saupe (b35) 2004 Lupinetti, Pernot, Monti, Giannini (b23) 2019; 113 Sundar, Silver, Gagvani, Dickinson (b27) 2003 Li, Lu, Li, Godil, Schreck, Aono, Burtscher, Chen, Chowdhury, Fang (b13) 2015; 131 Zehtaban, Roller (b22) 2013; 10 El-Mehalawi, Miller (b26) 2003; 35 Cha (b51) 2007; 1 Kazhdan, Funkhouser, Rusinkiewicz (b86) 2003 Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A. Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, p. 1–9. Tatsuma, Aono (b76) 2009; 25 Zoph B, Vasudevan V, Shlens J, Le QV. Learning transferable architectures for scalable image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, p. 8697–8710. Leifman G, Katz S, Tal A, Meir R. Signatures of 3D models for retrieval. In: Proceedings of the 4th Israel-Korea bi-national conference on geometric modeling and computer graphics, 2003, p. 159–63. He, Zhang, Ren, Sun (b38) 2016 Kim, Yeo, Lee, Mun (b47) 2020; 123 Zhang X, Zhou X, Lin M, Sun J. Shufflenet: An extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, p. 6848–56. Kim, Yeo, Cha, Mun (b46) 2021; 80 Chen, Fang, Yu, Tang (b74) 2015; 74 Zou, Ip, Wu, Chen, Yung, Chan (b82) 2014; 69 Shilane, Min, Kazhdan, Funkhouser (b54) 2004 Bai, Bai, Zhu, Latecki (b67) 2015; 37 Iyer, Jayanti, Lou, Kalyanaraman, Ramani (b5) 2005; 37 Xiao, Lai, Zhang, Li, Gao (b39) 2020; 6 Kalyanaraman, Iyer, Ramani (b48) 2006; 38 Madelmis, Daras, Tzovaras, Strintzis (b16) 2008 Krizhevsky, Sutskever, Hinton (b37) 2017; 60 Pan, You, Liu, Chen (b81) 2011; 32 Osada, Funkhouser, Chazelle, Dobkin (b88) 2002; 21 Ohbuchi, Minamitani, Takei (b89) 2005; 23 Alcantarilla, Bartoli, Davison (b31) 2012 Chowdhury, Tatsuma, Aono (b65) 2013 Bai, Rao, Wang (b77) 2014; 23 Kuo, Cheng (b87) 2007; 40 Qin, Jia, Qin (b17) 2008 Mehrdad, Ebrahimnezhad (b69) 2015; 9 Kim, Chi, Hu, Huang, Ramani (b90) 2020 Bespalov D, Ip CY, Regli WC, Shaffer J. Benchmarking CAD search techniques. In: Proceedings of the 2005 ACM symposium on solid and physical modeling, 2005, p. 275–86. Redmon, Farhadi (b63) 2018 Hilaga, Shinagawa, Kohmura, Kunii (b28) 2001 Papadakis, Pratikakis, Theoharis, Passalis, Perantonis (b80) 2008 Godil A, Lian Z, Dutagaci H, Fang R, Vanamali T, Cheung CP. Benchmarks, performance evaluation and contests for 3D shape retrieval. In: Proceedings of the 10th performance metrics for intelligent systems workshop, 2010, p. 42–7. Sfikas, Theoharis, Pratikakis (b71) 2014; 30 10.1016/j.cad.2022.103417_b10 10.1016/j.cad.2022.103417_b55 Redmon (10.1016/j.cad.2022.103417_b63) 2018 Vajna (10.1016/j.cad.2022.103417_b8) 2018 10.1016/j.cad.2022.103417_b12 10.1016/j.cad.2022.103417_b56 Sfikas (10.1016/j.cad.2022.103417_b71) 2014; 30 (10.1016/j.cad.2022.103417_b18) 2022 Yavartanoo (10.1016/j.cad.2022.103417_b44) 2018 10.1016/j.cad.2022.103417_b58 El-Mehalawi (10.1016/j.cad.2022.103417_b26) 2003; 35 10.1016/j.cad.2022.103417_b59 Qi (10.1016/j.cad.2022.103417_b42) 2016 Kuo (10.1016/j.cad.2022.103417_b87) 2007; 40 (10.1016/j.cad.2022.103417_b7) 2022 Iandola (10.1016/j.cad.2022.103417_b60) 2016 Li (10.1016/j.cad.2022.103417_b14) 2014; 119 Székely (10.1016/j.cad.2022.103417_b52) 2007 Bickel (10.1016/j.cad.2022.103417_b43) 2021; 96 Iyer (10.1016/j.cad.2022.103417_b4) 2005; 37 Osada (10.1016/j.cad.2022.103417_b88) 2002; 21 Zehtaban (10.1016/j.cad.2022.103417_b22) 2013; 10 Lupinetti (10.1016/j.cad.2022.103417_b23) 2019; 113 Cha (10.1016/j.cad.2022.103417_b51) 2007; 1 Sundar (10.1016/j.cad.2022.103417_b27) 2003 10.1016/j.cad.2022.103417_b53 Ankerst (10.1016/j.cad.2022.103417_b24) 1999 Li (10.1016/j.cad.2022.103417_b13) 2015; 131 10.1016/j.cad.2022.103417_b68 Sfikas (10.1016/j.cad.2022.103417_b73) 2011; 91 Spruegel (10.1016/j.cad.2022.103417_b45) 2021; 8 Vranic (10.1016/j.cad.2022.103417_b83) 2005 Kim (10.1016/j.cad.2022.103417_b47) 2020; 123 Cardone (10.1016/j.cad.2022.103417_b3) 2003; 3 Zou (10.1016/j.cad.2022.103417_b82) 2014; 69 (10.1016/j.cad.2022.103417_b19) 2022 Godil (10.1016/j.cad.2022.103417_b57) 2020 Chen (10.1016/j.cad.2022.103417_b74) 2015; 74 Lian (10.1016/j.cad.2022.103417_b30) 2010 Lecun (10.1016/j.cad.2022.103417_b36) 1998; 86 Chaudhari (10.1016/j.cad.2022.103417_b6) 2019 Madelmis (10.1016/j.cad.2022.103417_b16) 2008 Pan (10.1016/j.cad.2022.103417_b81) 2011; 32 Loncaric (10.1016/j.cad.2022.103417_b9) 1998; 31 Kim (10.1016/j.cad.2022.103417_b46) 2021; 80 Kalyanaraman (10.1016/j.cad.2022.103417_b48) 2006; 38 Tatsuma (10.1016/j.cad.2022.103417_b76) 2009; 25 10.1016/j.cad.2022.103417_b61 10.1016/j.cad.2022.103417_b62 Papadakis (10.1016/j.cad.2022.103417_b80) 2008 Ohbuchi (10.1016/j.cad.2022.103417_b89) 2005; 23 10.1016/j.cad.2022.103417_b64 Xiao (10.1016/j.cad.2022.103417_b39) 2020; 6 Alcantarilla (10.1016/j.cad.2022.103417_b31) 2012 Shih (10.1016/j.cad.2022.103417_b75) 2017; 15 Liu (10.1016/j.cad.2022.103417_b33) 2012 Tatsuma (10.1016/j.cad.2022.103417_b66) 2009; 25 Li (10.1016/j.cad.2022.103417_b32) 2013 Bai (10.1016/j.cad.2022.103417_b67) 2015; 37 Qin (10.1016/j.cad.2022.103417_b17) 2008 Bai (10.1016/j.cad.2022.103417_b77) 2014; 23 Iyer (10.1016/j.cad.2022.103417_b5) 2005; 37 Singh (10.1016/j.cad.2022.103417_b50) 2013; 67 Tangelder (10.1016/j.cad.2022.103417_b21) 2008; 39 Horn (10.1016/j.cad.2022.103417_b25) 1984; 72 Hou (10.1016/j.cad.2022.103417_b85) 2007 Bustos (10.1016/j.cad.2022.103417_b15) 2005; 37 Stenholm (10.1016/j.cad.2022.103417_b2) 2019; 39 Vranic (10.1016/j.cad.2022.103417_b35) 2004 Chen (10.1016/j.cad.2022.103417_b29) 2003 Zhu (10.1016/j.cad.2022.103417_b78) 2016; 204 Chowdhury (10.1016/j.cad.2022.103417_b65) 2013 (10.1016/j.cad.2022.103417_b20) 2022 Krizhevsky (10.1016/j.cad.2022.103417_b37) 2017; 60 He (10.1016/j.cad.2022.103417_b38) 2016 McWherter (10.1016/j.cad.2022.103417_b11) 2001 Hilaga (10.1016/j.cad.2022.103417_b28) 2001 Aono (10.1016/j.cad.2022.103417_b70) 2013 Kazhdan (10.1016/j.cad.2022.103417_b86) 2003 Kim (10.1016/j.cad.2022.103417_b90) 2020 Shilane (10.1016/j.cad.2022.103417_b54) 2004 Mehrdad (10.1016/j.cad.2022.103417_b69) 2015; 9 Bae (10.1016/j.cad.2022.103417_b84) 2013; 29 Pakkanen (10.1016/j.cad.2022.103417_b1) 2016; 50 Papadakis (10.1016/j.cad.2022.103417_b34) 2010; 89 Zhuang (10.1016/j.cad.2022.103417_b72) 2017; 2017 Li (10.1016/j.cad.2022.103417_b79) 2013; 62 10.1016/j.cad.2022.103417_b40 10.1016/j.cad.2022.103417_b41 Deng (10.1016/j.cad.2022.103417_b49) 2009 |
| References_xml | – start-page: 249 year: 2008 end-page: 256 ident: b17 article-title: Content based 3D model retrieval: A survey publication-title: 2008 international workshop on content-based multimedia indexing – volume: 23 start-page: 70 year: 2005 ident: b89 article-title: Shape-similarity search of 3D models by using enhanced shape functions publication-title: Int J Comput Appl Technol – year: 2008 ident: b80 article-title: 3D object retrieval using an efficient and compact hybrid shape descriptor publication-title: Eurographics workshop on 3d object retrieval – volume: 6 start-page: 113 year: 2020 end-page: 133 ident: b39 article-title: A survey on deep geometry learning: From a representation perspective publication-title: Comput Vis Media – reference: Godil A, Lian Z, Dutagaci H, Fang R, Vanamali T, Cheung CP. Benchmarks, performance evaluation and contests for 3D shape retrieval. In: Proceedings of the 10th performance metrics for intelligent systems workshop, 2010, p. 42–7. – reference: Leifman G, Katz S, Tal A, Meir R. Signatures of 3D models for retrieval. In: Proceedings of the 4th Israel-Korea bi-national conference on geometric modeling and computer graphics, 2003, p. 159–63. – reference: Cicirello VA, Regli WC. Resolving non-uniqueness in design feature histories. In: Proceedings of the fifth ACM symposium on solid modeling and applications, 1999, p. 76–84. – volume: 25 start-page: 785 year: 2009 end-page: 804 ident: b76 article-title: Multi-Fourier spectra descriptor and augmentation with spectral clustering for 3D shape retrieval publication-title: Vis Comput – year: 2020 ident: b57 article-title: SHREC’14 track: Large scale comprehensive 3D shape retrieval – reference: Getto R, Kuijper A, Fellner DW. Unsupervised 3D object retrieval with parameter-free hierarchical clustering. In: Proceedings of the computer graphics international conference, 2017, p. 1–6. – volume: 72 start-page: 1671 year: 1984 end-page: 1686 ident: b25 article-title: Extended gaussian images publication-title: Proc IEEE – reference: Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, p. 2818–2826. – start-page: 19 year: 2013 end-page: 22 ident: b65 article-title: A New Shape Descriptor based on Local and Global Feature for 3D Shape Retrieval – start-page: 853 year: 2008 end-page: 859 ident: b16 article-title: Three dimensional content-based search and retrieval of CAD objects publication-title: Encyclopedia of multimedia – volume: 123 year: 2020 ident: b47 article-title: Deep-learning-based retrieval of piping component catalogs for plant 3D CAD model reconstruction publication-title: Comput Ind – year: 2019 ident: b6 article-title: Similarity in engineering design: A knowledge-based approach publication-title: International design engineering technical conferences and computers and information in engineering conference, Vol. 59278 – volume: 35 start-page: 95 year: 2003 end-page: 105 ident: b26 article-title: A database system of mechanical components based on geometric and topological similarity. Part II: indexing, retrieval, matching, and similarity assessment publication-title: Comput Aided Des – volume: 1 year: 2007 ident: b51 article-title: Comprehensive survey on distance/similarity measures be-tween probability density functions publication-title: City – volume: 29 start-page: 555 year: 2013 end-page: 564 ident: b84 article-title: Content-based 3D model retrieval using a single depth image from a low-cost 3D camera publication-title: Vis Comput – reference: Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A. Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, p. 1–9. – volume: 23 start-page: 3935 year: 2014 end-page: 3949 ident: b77 article-title: Shape vocabulary: a robust and efficient shape representation for shape matching publication-title: IEEE Trans Image Process – volume: 69 start-page: 799 year: 2014 end-page: 818 ident: b82 article-title: A novel 3D model retrieval approach using combined shape distribution publication-title: Multimedia Tools Appl – year: 2001 ident: b28 article-title: Topology matching for fully automatic similarity estimation of 3D shapes publication-title: Proceedings of the 28th annual conference on computer graphics and interactive techniques - SIGGRAPH ’01 – volume: 204 start-page: 41 year: 2016 end-page: 50 ident: b78 article-title: Deep learning representation using autoencoder for 3D shape retrieval publication-title: Neurocomputing – year: 2018 ident: b8 article-title: CAx für ingenieure: Eine praxisbezogene einführung – reference: Su H, Maji S, Kalogerakis E, Learned-Miller E. Multi-view convo-lutional neural networks for 3d shape recognition. In: Proceedings of the IEEE international conference on computer vision, 2015, p. 945–53. – start-page: 156 year: 2003 end-page: 164 ident: b86 article-title: Rotation invariant spherical harmonic representation of 3 d shape descriptors publication-title: Symposium on geometry processing, Vol. 6 – start-page: 214 year: 2012 end-page: 227 ident: b31 article-title: KAZE features publication-title: European conference on computer vision – start-page: 207 year: 1999 end-page: 226 ident: b24 article-title: 3D shape histograms for similarity search and classification in spatial databases publication-title: Advances in spatial databases – volume: 38 start-page: 939 year: 2006 end-page: 953 ident: b48 article-title: Developing an engineering shape benchmark for CAD models publication-title: Comput Aided Des – volume: 32 start-page: 787 year: 2011 end-page: 794 ident: b81 article-title: 3D shape retrieval by Poisson histogram publication-title: Pattern Recognit Lett – volume: 31 start-page: 983 year: 1998 end-page: 1001 ident: b9 article-title: A survey of shape analysis techniques publication-title: Pattern Recognit – year: 2022 ident: b18 article-title: 3Dsémantix – volume: 60 start-page: 84 year: 2017 end-page: 90 ident: b37 article-title: ImageNet classification with deep convolutional neural networks publication-title: Commun. ACM – year: 2010 ident: b30 article-title: Visual similarity based 3D shape retrieval using bag-of-features publication-title: 2010 shape modeling international conference – volume: 10 start-page: 789 year: 2013 end-page: 802 ident: b22 article-title: Beyond similarity comparison: Intelligent data retrieval for CAD/CAM designs publication-title: Comput Aided Des Appl – year: 2009 ident: b49 article-title: ImageNet: A large-scale hierarchical image database publication-title: 2009 IEEE conference on computer vision and pattern recognition – volume: 40 start-page: 742 year: 2007 end-page: 755 ident: b87 article-title: 3D model retrieval using principal plane analysis and dynamic programming publication-title: Pattern Recognit – volume: 113 start-page: 62 year: 2019 end-page: 81 ident: b23 article-title: Content-based CAD assembly model retrieval: Survey and future challenges publication-title: Comput Aided Des – year: 2004 ident: b54 article-title: The princeton shape benchmark publication-title: Proceedings shape modeling applications, 2004 – year: 2016 ident: b60 article-title: Squeezenet: AlexNet-level accuracy with 50x fewer parameters and¡ 0.5 MB model size – start-page: 223 year: 2003 end-page: 232 ident: b29 article-title: On visual similarity based 3D model retrieval publication-title: Computer graphics forum, Vol. 22 – start-page: 691 year: 2018 end-page: 706 ident: b44 article-title: Spnet: Deep 3d ob-ject classification and retrieval using stereographic projection publication-title: Asian confer-ence on computer vision – year: 2018 ident: b63 article-title: Yolov3: An incremental improvement – volume: 80 start-page: 10859 year: 2021 end-page: 10880 ident: b46 article-title: A method of generating depth images for view-based shape retrieval of 3D CAD models from partial point clouds publication-title: Multimedia Tools Appl – volume: 21 start-page: 807 year: 2002 end-page: 832 ident: b88 article-title: Shape distributions publication-title: ACM Trans Graph – volume: 39 start-page: 128 year: 2019 end-page: 145 ident: b2 article-title: A framework of practices supporting the reuse of technological knowledge publication-title: Environ Syst Decis – start-page: 385 year: 2007 end-page: 388 ident: b85 article-title: Using enhanced shape distributions to compare CAD models publication-title: Advances in multimedia information processing – PCM 2007 – volume: 8 start-page: 298 year: 2021 end-page: 315 ident: b45 article-title: Approach and application to transfer heterogeneous simulation data from finite element anal-ysis to neural networks publication-title: J Comput Des Eng – reference: Zhang X, Zhou X, Lin M, Sun J. Shufflenet: An extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, p. 6848–56. – reference: Zoph B, Vasudevan V, Shlens J, Le QV. Learning transferable architectures for scalable image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, p. 8697–8710. – volume: 9 start-page: 990 year: 2015 end-page: 1005 ident: b69 article-title: 3D object retrieval based on histogram of local orientation using one-shot score support vector machine publication-title: Front Comput Sci – volume: 96 start-page: 133 year: 2021 end-page: 138 ident: b43 article-title: Comparing CAD part models for geometrical similarity: A concept using machine learning algo-rithms publication-title: Proc CIRP – volume: 37 start-page: 509 year: 2005 end-page: 530 ident: b5 article-title: Three-dimensional shape searching: state-of-the-art review and future trends publication-title: Comput Aided Des – reference: Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. 2017. Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, p. 4700–8. – year: 2022 ident: b19 article-title: iSEEK corporation – volume: 62 start-page: 821 year: 2013 end-page: 846 ident: b79 article-title: 3D model retrieval using hybrid features and class information publication-title: Multimedia Tools Appl – volume: 37 start-page: 1435 year: 2005 end-page: 1446 ident: b4 article-title: Shape-based searching for product lifecycle applications publication-title: Comput Aided Des – volume: 89 start-page: 177 year: 2010 end-page: 192 ident: b34 article-title: PANORAMA: A 3D shape descriptor based on panoramic views for unsupervised 3D object retrieval publication-title: Int J Comput Vis – volume: 3 start-page: 109 year: 2003 end-page: 118 ident: b3 article-title: A survey of shape similarity assessment algorithms for product design and manufacturing applications publication-title: J Comput Inf Sci Eng – year: 2005 ident: b83 article-title: Desire: A composite 3D-shape descriptor publication-title: 2005 IEEE international conference on multimedia and expo – volume: 119 start-page: 57 year: 2014 end-page: 80 ident: b14 article-title: A comparison of methods for sketch-based 3D shape retrieval publication-title: Comput Vis Image Underst – volume: 2017 start-page: 1 year: 2017 end-page: 12 ident: b72 article-title: A novel 3D CAD model retrieval method based on vertices classification and weights combination optimization publication-title: Math Probl Eng – year: 2004 ident: b35 article-title: 3D model retrieval – year: 2007 ident: b52 article-title: Measuring and testing dependence by jayanti – volume: 74 start-page: 4907 year: 2015 end-page: 4925 ident: b74 article-title: 3D CAD model retrieval based on the combination of features publication-title: Multimedia Tools Appl – volume: 25 start-page: 785 year: 2009 end-page: 804 ident: b66 article-title: Multi-Fourier spectra descriptor and augmentation with spectral clustering for 3D shape retrieval publication-title: Vis Comput – volume: 30 start-page: 1261 year: 2014 end-page: 1274 ident: b71 article-title: Pose normalization of 3D models via reflective symmetry on panoramic views publication-title: Vis Comput – volume: 91 start-page: 262 year: 2011 end-page: 279 ident: b73 article-title: ROSy+: 3D object pose normalization based on PCA and reflective object symmetry with application in 3D object retrieval publication-title: Int J Comput Vis – year: 2022 ident: b20 article-title: Shape search – volume: 67 start-page: 13 year: 2013 end-page: 17 ident: b50 article-title: K-means with three different distance metrics publication-title: Int J Comput Appl – volume: 37 start-page: 2361 year: 2015 end-page: 2373 ident: b67 article-title: 3D shape matching via two layer coding publication-title: IEEE Trans Pattern Anal Mach Intell – volume: 50 start-page: 8 year: 2016 end-page: 13 ident: b1 article-title: Achieving benefits with design reuse in manufacturing industry publication-title: Proc CIRP – volume: 39 start-page: 441 year: 2008 end-page: 471 ident: b21 article-title: A survey of content based 3D shape retrieval methods publication-title: Multimedia Tools Appl – reference: Fang Y, Xie J, Dai G, Wang M, Zhu F, Xu T, Wong E. 3d deep shape descriptor. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, p. 2319–28. – start-page: 175 year: 2020 end-page: 191 ident: b90 article-title: A large-scale annotated mechanical components benchmark for classification and retrieval tasks with deep neural networks publication-title: Computer vision – ECCV 2020 – volume: 15 start-page: 169 year: 2017 end-page: 177 ident: b75 article-title: Three-dimensional model retrieval using dynamic multi-descriptor fusion publication-title: J Electron Sci Technol – start-page: 172 year: 2013 end-page: 179 ident: b32 article-title: Retrieving 3D model using compound-eye vis-ual representation publication-title: Computer-aided design and computer graphics (CAD/ Graphics) 2013 international conference on – year: 2016 ident: b38 article-title: Deep residual learning for image recognition publication-title: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) – start-page: 303 year: 2001 end-page: 312 ident: b11 article-title: Transformation invariant shape similarity comparison of solid models publication-title: International design engineering technical conferences and computers and information in engineering conference, Vol. 80241 – year: 2016 ident: b42 article-title: PointNet: Deep learning on point sets for 3D classification and segmentation – year: 2022 ident: b7 article-title: Dudenredaktion (Hrsg.). (o.j.), aehnlich. Duden online – year: 2013 ident: b70 article-title: 3D shape retrieval focused on holes and surface roughness publication-title: 2013 Asia-Pacific signal and information processing association annual summit and conference – start-page: 130 year: 2003 end-page: 139 ident: b27 article-title: Skeleton based shape matching and retrieval publication-title: 2003 shape modeling international – year: 2012 ident: b33 article-title: A survey of recent view-based 3D model retrieval methods – volume: 37 start-page: 345 year: 2005 end-page: 387 ident: b15 article-title: Feature-based similarity search in 3D object databases publication-title: ACM Comput Surv – reference: Bespalov D, Ip CY, Regli WC, Shaffer J. Benchmarking CAD search techniques. In: Proceedings of the 2005 ACM symposium on solid and physical modeling, 2005, p. 275–86. – volume: 131 start-page: 1 year: 2015 end-page: 27 ident: b13 article-title: A comparison of 3D shape retrieval methods based on a large-scale benchmark supporting multimodal queries publication-title: Comput Vis Image Underst – reference: Jagadeesan P, Wenzel J, Corney JR, Yan XT, Sherlock A, Torres-Sanchez C, Regli W. Validation of purdue engineer-ing shape benchmark clusters by crowdsourcing. In: Proceedings of the International Conference on Product Lifecycle Management, Bath, UK, 2009. – volume: 86 start-page: 2278 year: 1998 end-page: 2324 ident: b36 article-title: Gradient-based learning applied to document recognition publication-title: Proc IEEE Inst Electr Electron Eng – volume: 8 start-page: 298 issue: 1 year: 2021 ident: 10.1016/j.cad.2022.103417_b45 article-title: Approach and application to transfer heterogeneous simulation data from finite element anal-ysis to neural networks publication-title: J Comput Des Eng – ident: 10.1016/j.cad.2022.103417_b10 doi: 10.1145/1060244.1060275 – year: 2018 ident: 10.1016/j.cad.2022.103417_b63 – volume: 50 start-page: 8 year: 2016 ident: 10.1016/j.cad.2022.103417_b1 article-title: Achieving benefits with design reuse in manufacturing industry publication-title: Proc CIRP doi: 10.1016/j.procir.2016.04.173 – start-page: 303 year: 2001 ident: 10.1016/j.cad.2022.103417_b11 article-title: Transformation invariant shape similarity comparison of solid models – start-page: 249 year: 2008 ident: 10.1016/j.cad.2022.103417_b17 article-title: Content based 3D model retrieval: A survey – start-page: 853 year: 2008 ident: 10.1016/j.cad.2022.103417_b16 article-title: Three dimensional content-based search and retrieval of CAD objects doi: 10.1007/978-0-387-78414-4_1 – ident: 10.1016/j.cad.2022.103417_b41 doi: 10.1109/ICCV.2015.114 – volume: 25 start-page: 785 issue: 8 year: 2009 ident: 10.1016/j.cad.2022.103417_b66 article-title: Multi-Fourier spectra descriptor and augmentation with spectral clustering for 3D shape retrieval publication-title: Vis Comput doi: 10.1007/s00371-008-0304-2 – volume: 86 start-page: 2278 issue: 11 year: 1998 ident: 10.1016/j.cad.2022.103417_b36 article-title: Gradient-based learning applied to document recognition publication-title: Proc IEEE Inst Electr Electron Eng doi: 10.1109/5.726791 – volume: 96 start-page: 133 year: 2021 ident: 10.1016/j.cad.2022.103417_b43 article-title: Comparing CAD part models for geometrical similarity: A concept using machine learning algo-rithms publication-title: Proc CIRP doi: 10.1016/j.procir.2021.01.065 – ident: 10.1016/j.cad.2022.103417_b68 doi: 10.1145/3095140.3095147 – year: 2001 ident: 10.1016/j.cad.2022.103417_b28 article-title: Topology matching for fully automatic similarity estimation of 3D shapes – year: 2010 ident: 10.1016/j.cad.2022.103417_b30 article-title: Visual similarity based 3D shape retrieval using bag-of-features – volume: 10 start-page: 789 issue: 5 year: 2013 ident: 10.1016/j.cad.2022.103417_b22 article-title: Beyond similarity comparison: Intelligent data retrieval for CAD/CAM designs publication-title: Comput Aided Des Appl doi: 10.3722/cadaps.2013.789-802 – volume: 23 start-page: 70 issue: 2/3/4 year: 2005 ident: 10.1016/j.cad.2022.103417_b89 article-title: Shape-similarity search of 3D models by using enhanced shape functions publication-title: Int J Comput Appl Technol doi: 10.1504/IJCAT.2005.006466 – start-page: 385 year: 2007 ident: 10.1016/j.cad.2022.103417_b85 article-title: Using enhanced shape distributions to compare CAD models – year: 2009 ident: 10.1016/j.cad.2022.103417_b49 article-title: ImageNet: A large-scale hierarchical image database – start-page: 214 year: 2012 ident: 10.1016/j.cad.2022.103417_b31 article-title: KAZE features – volume: 37 start-page: 1435 issue: 13 year: 2005 ident: 10.1016/j.cad.2022.103417_b4 article-title: Shape-based searching for product lifecycle applications publication-title: Comput Aided Des doi: 10.1016/j.cad.2005.02.011 – volume: 31 start-page: 983 issue: 8 year: 1998 ident: 10.1016/j.cad.2022.103417_b9 article-title: A survey of shape analysis techniques publication-title: Pattern Recognit doi: 10.1016/S0031-2023(97)00122-2 – ident: 10.1016/j.cad.2022.103417_b40 doi: 10.1109/CVPR.2015.7298845 – year: 2016 ident: 10.1016/j.cad.2022.103417_b60 – year: 2016 ident: 10.1016/j.cad.2022.103417_b38 article-title: Deep residual learning for image recognition – volume: 62 start-page: 821 issue: 3 year: 2013 ident: 10.1016/j.cad.2022.103417_b79 article-title: 3D model retrieval using hybrid features and class information publication-title: Multimedia Tools Appl doi: 10.1007/s11042-011-0873-3 – volume: 74 start-page: 4907 issue: 13 year: 2015 ident: 10.1016/j.cad.2022.103417_b74 article-title: 3D CAD model retrieval based on the combination of features publication-title: Multimedia Tools Appl doi: 10.1007/s11042-013-1850-9 – volume: 25 start-page: 785 issue: 8 year: 2009 ident: 10.1016/j.cad.2022.103417_b76 article-title: Multi-Fourier spectra descriptor and augmentation with spectral clustering for 3D shape retrieval publication-title: Vis Comput doi: 10.1007/s00371-008-0304-2 – volume: 32 start-page: 787 issue: 6 year: 2011 ident: 10.1016/j.cad.2022.103417_b81 article-title: 3D shape retrieval by Poisson histogram publication-title: Pattern Recognit Lett doi: 10.1016/j.patrec.2011.01.003 – start-page: 175 year: 2020 ident: 10.1016/j.cad.2022.103417_b90 article-title: A large-scale annotated mechanical components benchmark for classification and retrieval tasks with deep neural networks – volume: 37 start-page: 509 issue: 5 year: 2005 ident: 10.1016/j.cad.2022.103417_b5 article-title: Three-dimensional shape searching: state-of-the-art review and future trends publication-title: Comput Aided Des doi: 10.1016/j.cad.2004.07.002 – volume: 91 start-page: 262 issue: 3 year: 2011 ident: 10.1016/j.cad.2022.103417_b73 article-title: ROSy+: 3D object pose normalization based on PCA and reflective object symmetry with application in 3D object retrieval publication-title: Int J Comput Vis doi: 10.1007/s11263-010-0395-x – year: 2005 ident: 10.1016/j.cad.2022.103417_b83 article-title: Desire: A composite 3D-shape descriptor – year: 2022 ident: 10.1016/j.cad.2022.103417_b19 – volume: 9 start-page: 990 issue: 6 year: 2015 ident: 10.1016/j.cad.2022.103417_b69 article-title: 3D object retrieval based on histogram of local orientation using one-shot score support vector machine publication-title: Front Comput Sci doi: 10.1007/s11704-015-4291-y – volume: 6 start-page: 113 issue: 2 year: 2020 ident: 10.1016/j.cad.2022.103417_b39 article-title: A survey on deep geometry learning: From a representation perspective publication-title: Comput Vis Media doi: 10.1007/s41095-020-0174-8 – volume: 39 start-page: 441 issue: 3 year: 2008 ident: 10.1016/j.cad.2022.103417_b21 article-title: A survey of content based 3D shape retrieval methods publication-title: Multimedia Tools Appl doi: 10.1007/s11042-007-0181-0 – start-page: 130 year: 2003 ident: 10.1016/j.cad.2022.103417_b27 article-title: Skeleton based shape matching and retrieval – ident: 10.1016/j.cad.2022.103417_b59 doi: 10.1109/CVPR.2018.00716 – volume: 37 start-page: 2361 issue: 12 year: 2015 ident: 10.1016/j.cad.2022.103417_b67 article-title: 3D shape matching via two layer coding publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2015.2424863 – volume: 131 start-page: 1 year: 2015 ident: 10.1016/j.cad.2022.103417_b13 article-title: A comparison of 3D shape retrieval methods based on a large-scale benchmark supporting multimodal queries publication-title: Comput Vis Image Underst doi: 10.1016/j.cviu.2014.10.006 – year: 2013 ident: 10.1016/j.cad.2022.103417_b70 article-title: 3D shape retrieval focused on holes and surface roughness – year: 2004 ident: 10.1016/j.cad.2022.103417_b54 article-title: The princeton shape benchmark – volume: 119 start-page: 57 year: 2014 ident: 10.1016/j.cad.2022.103417_b14 article-title: A comparison of methods for sketch-based 3D shape retrieval publication-title: Comput Vis Image Underst doi: 10.1016/j.cviu.2013.11.008 – volume: 38 start-page: 939 issue: 9 year: 2006 ident: 10.1016/j.cad.2022.103417_b48 article-title: Developing an engineering shape benchmark for CAD models publication-title: Comput Aided Des doi: 10.1016/j.cad.2006.06.007 – start-page: 156 year: 2003 ident: 10.1016/j.cad.2022.103417_b86 article-title: Rotation invariant spherical harmonic representation of 3 d shape descriptors – start-page: 223 year: 2003 ident: 10.1016/j.cad.2022.103417_b29 article-title: On visual similarity based 3D model retrieval – year: 2012 ident: 10.1016/j.cad.2022.103417_b33 – volume: 15 start-page: 169 issue: 2 year: 2017 ident: 10.1016/j.cad.2022.103417_b75 article-title: Three-dimensional model retrieval using dynamic multi-descriptor fusion publication-title: J Electron Sci Technol – volume: 30 start-page: 1261 issue: 11 year: 2014 ident: 10.1016/j.cad.2022.103417_b71 article-title: Pose normalization of 3D models via reflective symmetry on panoramic views publication-title: Vis Comput doi: 10.1007/s00371-014-0935-4 – year: 2019 ident: 10.1016/j.cad.2022.103417_b6 article-title: Similarity in engineering design: A knowledge-based approach – ident: 10.1016/j.cad.2022.103417_b12 doi: 10.1145/304012.304020 – year: 2016 ident: 10.1016/j.cad.2022.103417_b42 – volume: 39 start-page: 128 issue: 2 year: 2019 ident: 10.1016/j.cad.2022.103417_b2 article-title: A framework of practices supporting the reuse of technological knowledge publication-title: Environ Syst Decis doi: 10.1007/s10669-019-09732-4 – year: 2020 ident: 10.1016/j.cad.2022.103417_b57 – ident: 10.1016/j.cad.2022.103417_b61 doi: 10.1109/CVPR.2016.308 – volume: 3 start-page: 109 issue: 2 year: 2003 ident: 10.1016/j.cad.2022.103417_b3 article-title: A survey of shape similarity assessment algorithms for product design and manufacturing applications publication-title: J Comput Inf Sci Eng doi: 10.1115/1.1577356 – volume: 113 start-page: 62 year: 2019 ident: 10.1016/j.cad.2022.103417_b23 article-title: Content-based CAD assembly model retrieval: Survey and future challenges publication-title: Comput Aided Des doi: 10.1016/j.cad.2019.03.005 – ident: 10.1016/j.cad.2022.103417_b55 doi: 10.1145/2377576.2377584 – volume: 37 start-page: 345 issue: 4 year: 2005 ident: 10.1016/j.cad.2022.103417_b15 article-title: Feature-based similarity search in 3D object databases publication-title: ACM Comput Surv doi: 10.1145/1118890.1118893 – start-page: 19 year: 2013 ident: 10.1016/j.cad.2022.103417_b65 – start-page: 172 year: 2013 ident: 10.1016/j.cad.2022.103417_b32 article-title: Retrieving 3D model using compound-eye vis-ual representation – year: 2022 ident: 10.1016/j.cad.2022.103417_b7 – volume: 72 start-page: 1671 issue: 12 year: 1984 ident: 10.1016/j.cad.2022.103417_b25 article-title: Extended gaussian images publication-title: Proc IEEE doi: 10.1109/PROC.1984.13073 – ident: 10.1016/j.cad.2022.103417_b53 – volume: 23 start-page: 3935 issue: 9 year: 2014 ident: 10.1016/j.cad.2022.103417_b77 article-title: Shape vocabulary: a robust and efficient shape representation for shape matching publication-title: IEEE Trans Image Process doi: 10.1109/TIP.2014.2336542 – volume: 40 start-page: 742 issue: 2 year: 2007 ident: 10.1016/j.cad.2022.103417_b87 article-title: 3D model retrieval using principal plane analysis and dynamic programming publication-title: Pattern Recognit doi: 10.1016/j.patcog.2006.06.006 – volume: 60 start-page: 84 issue: 6 year: 2017 ident: 10.1016/j.cad.2022.103417_b37 article-title: ImageNet classification with deep convolutional neural networks publication-title: Commun. ACM doi: 10.1145/3065386 – volume: 29 start-page: 555 issue: 6–8 year: 2013 ident: 10.1016/j.cad.2022.103417_b84 article-title: Content-based 3D model retrieval using a single depth image from a low-cost 3D camera publication-title: Vis Comput doi: 10.1007/s00371-013-0819-z – volume: 1 issue: 2 year: 2007 ident: 10.1016/j.cad.2022.103417_b51 article-title: Comprehensive survey on distance/similarity measures be-tween probability density functions publication-title: City – year: 2008 ident: 10.1016/j.cad.2022.103417_b80 article-title: 3D object retrieval using an efficient and compact hybrid shape descriptor – year: 2004 ident: 10.1016/j.cad.2022.103417_b35 – volume: 2017 start-page: 1 year: 2017 ident: 10.1016/j.cad.2022.103417_b72 article-title: A novel 3D CAD model retrieval method based on vertices classification and weights combination optimization publication-title: Math Probl Eng doi: 10.1155/2017/6049750 – volume: 123 year: 2020 ident: 10.1016/j.cad.2022.103417_b47 article-title: Deep-learning-based retrieval of piping component catalogs for plant 3D CAD model reconstruction publication-title: Comput Ind doi: 10.1016/j.compind.2020.103320 – ident: 10.1016/j.cad.2022.103417_b62 doi: 10.1109/CVPR.2015.7298594 – volume: 89 start-page: 177 issue: 2–3 year: 2010 ident: 10.1016/j.cad.2022.103417_b34 article-title: PANORAMA: A 3D shape descriptor based on panoramic views for unsupervised 3D object retrieval publication-title: Int J Comput Vis doi: 10.1007/s11263-009-0281-6 – year: 2022 ident: 10.1016/j.cad.2022.103417_b18 – year: 2007 ident: 10.1016/j.cad.2022.103417_b52 – year: 2018 ident: 10.1016/j.cad.2022.103417_b8 – volume: 35 start-page: 95 issue: 1 year: 2003 ident: 10.1016/j.cad.2022.103417_b26 article-title: A database system of mechanical components based on geometric and topological similarity. Part II: indexing, retrieval, matching, and similarity assessment publication-title: Comput Aided Des doi: 10.1016/S0010-4485(01)00178-6 – volume: 204 start-page: 41 year: 2016 ident: 10.1016/j.cad.2022.103417_b78 article-title: Deep learning representation using autoencoder for 3D shape retrieval publication-title: Neurocomputing doi: 10.1016/j.neucom.2015.08.127 – ident: 10.1016/j.cad.2022.103417_b58 doi: 10.1109/CVPR.2017.243 – ident: 10.1016/j.cad.2022.103417_b64 doi: 10.1109/CVPR.2018.00907 – volume: 69 start-page: 799 issue: 3 year: 2014 ident: 10.1016/j.cad.2022.103417_b82 article-title: A novel 3D model retrieval approach using combined shape distribution publication-title: Multimedia Tools Appl doi: 10.1007/s11042-012-1130-0 – volume: 80 start-page: 10859 issue: 7 year: 2021 ident: 10.1016/j.cad.2022.103417_b46 article-title: A method of generating depth images for view-based shape retrieval of 3D CAD models from partial point clouds publication-title: Multimedia Tools Appl doi: 10.1007/s11042-020-10283-z – year: 2022 ident: 10.1016/j.cad.2022.103417_b20 – volume: 67 start-page: 13 issue: 10 year: 2013 ident: 10.1016/j.cad.2022.103417_b50 article-title: K-means with three different distance metrics publication-title: Int J Comput Appl – start-page: 207 year: 1999 ident: 10.1016/j.cad.2022.103417_b24 article-title: 3D shape histograms for similarity search and classification in spatial databases – ident: 10.1016/j.cad.2022.103417_b56 – volume: 21 start-page: 807 issue: 4 year: 2002 ident: 10.1016/j.cad.2022.103417_b88 article-title: Shape distributions publication-title: ACM Trans Graph doi: 10.1145/571647.571648 – start-page: 691 year: 2018 ident: 10.1016/j.cad.2022.103417_b44 article-title: Spnet: Deep 3d ob-ject classification and retrieval using stereographic projection |
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| SubjectTerms | 3D object retrieval Autoencoder Deep Learning Part alignment Projection method Shape retrieval |
| Title | A Novel Shape Retrieval Method for 3D Mechanical Components Based on Object Projection, Pre-Trained Deep Learning Models and Autoencoder |
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