{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T01:58:54Z","timestamp":1781056734235,"version":"3.54.1"},"reference-count":65,"publisher":"SAGE Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ICA"],"published-print":{"date-parts":[[2019,11,27]]},"DOI":"10.3233\/ica-190608","type":"journal-article","created":{"date-parts":[[2019,10,1]],"date-time":"2019-10-01T14:00:47Z","timestamp":1569938447000},"page":"57-75","source":"Crossref","is-referenced-by-count":41,"title":["Pointwise geometric and semantic learning network on 3D point clouds"],"prefix":"10.1177","volume":"27","author":[{"given":"Dejun","family":"Zhang","sequence":"first","affiliation":[{"name":"Faculty of Information Engineering, China University of Geosciences, Wuhan, Hubei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fazhi","family":"He","sequence":"additional","affiliation":[{"name":"School of Computer Science, Wuhan University, Wuhan, Hubei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhigang","family":"Tu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, Hubei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lu","family":"Zou","sequence":"additional","affiliation":[{"name":"School of Data Science, University of Science and Technology of China, Anhui, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yilin","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science, Wuhan University, Wuhan, Hubei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","reference":[{"issue":"6","key":"10.3233\/ICA-190608_ref1","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1145\/2980179.2980238","article-title":"A scalable active framework for region annotation in 3d shape collections","volume":"35","author":"Yi","year":"2016","journal-title":"ACM Transactions on Graphics (TOG)."},{"issue":"1","key":"10.3233\/ICA-190608_ref2","doi-asserted-by":"crossref","first-page":"31","DOI":"10.3233\/ICA-150499","article-title":"Quantitative optimization of interoperability during feature-based data exchange","volume":"23","author":"Zhang","year":"2016","journal-title":"Integrated Computer-Aided Engineering."},{"issue":"3","key":"10.3233\/ICA-190608_ref3","doi-asserted-by":"crossref","first-page":"261","DOI":"10.3233\/ICA-170544","article-title":"An efficient approach to directly compute the exact Hausdorff distance for 3D point sets","volume":"24","author":"Zhang","year":"2017","journal-title":"Integrated Computer-Aided Engineering."},{"issue":"10","key":"10.3233\/ICA-190608_ref4","doi-asserted-by":"crossref","first-page":"885","DOI":"10.1111\/mice.12375","article-title":"Autonomous UAVs for structural health monitoring using deep learning and an ultrasonic beacon system with geo-tagging","volume":"33","author":"Kang","year":"2018","journal-title":"Computer-Aided Civil and Infrastructure Engineering."},{"key":"10.3233\/ICA-190608_ref5","doi-asserted-by":"crossref","unstructured":"Rafiei MH, Khushefati WH, Demirboga R, Adeli H. Supervised deep restricted boltzmann machine for estimation of concrete. ACI Materials Journal. 2017; 114(2).","DOI":"10.14359\/51689560"},{"key":"10.3233\/ICA-190608_ref6","doi-asserted-by":"crossref","first-page":"598","DOI":"10.1016\/j.engstruct.2017.10.070","article-title":"A novel unsupervised deep learning model for global and local health condition assessment of structures","volume":"156","author":"Rafiei","year":"2018","journal-title":"Engineering Structures."},{"issue":"2","key":"10.3233\/ICA-190608_ref7","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1111\/0885-9507.00219","article-title":"Neural networks in civil engineering: 1989\u20132000","volume":"16","author":"Adeli","year":"2001","journal-title":"Computer-Aided Civil and Infrastructure Engineering."},{"issue":"1","key":"10.3233\/ICA-190608_ref8","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1061\/(ASCE)0733-9445(2006)132:1(102)","article-title":"Dynamic fuzzy wavelet neural network model for structural system identification","volume":"132","author":"Adeli","year":"2006","journal-title":"Journal of Structural Engineering."},{"issue":"7","key":"10.3233\/ICA-190608_ref9","doi-asserted-by":"crossref","first-page":"1018","DOI":"10.1016\/j.neunet.2009.05.003","article-title":"A probabilistic neural network for earthquake magnitude prediction","volume":"22","author":"Adeli","year":"2009","journal-title":"Neural Networks."},{"issue":"12","key":"10.3233\/ICA-190608_ref10","doi-asserted-by":"crossref","first-page":"1090","DOI":"10.1111\/mice.12412","article-title":"Automatic pixel-level crack detection and measurement using fully convolutional network","volume":"33","author":"Yang","year":"2018","journal-title":"Computer-Aided Civil and Infrastructure Engineering."},{"issue":"9","key":"10.3233\/ICA-190608_ref11","doi-asserted-by":"crossref","first-page":"731","DOI":"10.1111\/mice.12334","article-title":"Autonomous structural visual inspection using region-based deep learning for detecting multiple damage types","volume":"33","author":"Cha","year":"2018","journal-title":"Computer-Aided Civil and Infrastructure Engineering."},{"issue":"8","key":"10.3233\/ICA-190608_ref12","doi-asserted-by":"crossref","first-page":"638","DOI":"10.1111\/mice.12367","article-title":"A fast detection method via region-based fully convolutional neural networks for shield tunnel lining defects","volume":"33","author":"Xue","year":"2018","journal-title":"Computer-Aided Civil and Infrastructure Engineering."},{"issue":"9","key":"10.3233\/ICA-190608_ref13","doi-asserted-by":"crossref","first-page":"748","DOI":"10.1111\/mice.12363","article-title":"Deep transfer learning for image-based structural damage recognition","volume":"33","author":"Gao","year":"2018","journal-title":"Computer-Aided Civil and Infrastructure Engineering."},{"issue":"6","key":"10.3233\/ICA-190608_ref14","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1111\/mice.12359","article-title":"Deep learning for accelerated seismic reliability analysis of transportation networks","volume":"33","author":"Nabian","year":"2018","journal-title":"Computer-Aided Civil and Infrastructure Engineering."},{"issue":"10","key":"10.3233\/ICA-190608_ref15","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1111\/mice.12376","article-title":"End-to-end deep learning methodology for real-time traffic network management","volume":"33","author":"Hashemi","year":"2018","journal-title":"Computer-Aided Civil and Infrastructure Engineering."},{"key":"10.3233\/ICA-190608_ref16","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2016. pp. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"issue":"10","key":"10.3233\/ICA-190608_ref17","doi-asserted-by":"crossref","first-page":"2222","DOI":"10.1109\/TNNLS.2016.2582924","article-title":"LSTM: a search space odyssey","volume":"28","author":"Greff","year":"2017","journal-title":"IEEE Transactions on Neural Networks and Learning Systems."},{"key":"10.3233\/ICA-190608_ref18","doi-asserted-by":"crossref","unstructured":"Wang Z, Yuan J. Simultaneously discovering and localizing common objects in wild images. IEEE Transactions on Image Processing. 2018; 27(9): 4503\u20134515.","DOI":"10.1109\/TIP.2018.2839901"},{"issue":"2","key":"10.3233\/ICA-190608_ref19","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1631\/jzus.C1300185","article-title":"A deep learning approach to the classification of 3D CAD models","volume":"15","author":"Qin","year":"2014","journal-title":"Journal of Zhejiang Universityence C."},{"issue":"8","key":"10.3233\/ICA-190608_ref20","doi-asserted-by":"crossref","first-page":"2154","DOI":"10.1109\/TMM.2014.2351788","article-title":"Learning high-level feature by deep belief networks for 3-D model retrieval and recognition","volume":"16","author":"Bu","year":"2014","journal-title":"IEEE Transactions on Multimedia."},{"key":"10.3233\/ICA-190608_ref21","doi-asserted-by":"crossref","unstructured":"Su H, Maji S, Kalogerakis E, Learned-Miller E. Multi-view convolutional neural networks for 3d shape recognition. In: Proceedings of the IEEE International Conference on Computer Vision; 2015. pp. 945\u2013953.","DOI":"10.1109\/ICCV.2015.114"},{"key":"10.3233\/ICA-190608_ref22","doi-asserted-by":"crossref","unstructured":"Qi CR, Su H, Nie\u00dfner M, Dai A, Yan M, Guibas LJ. Volumetric and multi-view cnns for object classification on 3d data. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2016. pp. 5648\u20135656.","DOI":"10.1109\/CVPR.2016.609"},{"key":"10.3233\/ICA-190608_ref23","doi-asserted-by":"crossref","unstructured":"Pang G, Neumann U. 3d point cloud object detection with multi-view convolutional neural network. In: 2016 23rd International Conference on Pattern Recognition (ICPR). IEEE; 2016. pp. 585\u2013590.","DOI":"10.1109\/ICPR.2016.7899697"},{"key":"10.3233\/ICA-190608_ref24","unstructured":"Wu Z, Song S, Khosla A, Yu F, Zhang L, Tang X, et al. 3d shapenets: A deep representation for volumetric shapes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2015. pp. 1912\u20131920."},{"key":"10.3233\/ICA-190608_ref25","doi-asserted-by":"crossref","unstructured":"Maturana D, Scherer S. VoxNet: A 3D Convolutional Neural Network for real-time object recognition. In: Ieee\/rsj International Conference on Intelligent Robots and Systems; 2015. pp. 922\u2013928.","DOI":"10.1109\/IROS.2015.7353481"},{"key":"10.3233\/ICA-190608_ref26","doi-asserted-by":"crossref","unstructured":"Riegler G, Osman Ulusoy A, Geiger A. Octnet: Learning deep 3d representations at high resolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2017. pp. 3577\u20133586.","DOI":"10.1109\/CVPR.2017.701"},{"issue":"4","key":"10.3233\/ICA-190608_ref27","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1145\/3072959.3073608","article-title":"O-cnn: octree-based convolutional neural networks for 3d shape analysis","volume":"36","author":"Wang","year":"2017","journal-title":"ACM Transactions on Graphics (TOG)."},{"key":"10.3233\/ICA-190608_ref28","unstructured":"Kazhdan M, Funkhouser T, Rusinkiewicz S. Rotation invariant spherical harmonic representation of 3 d shape descriptors. In: Symposium on Geometry Processing. Vol. 6; 2003. pp. 156\u2013164."},{"issue":"4","key":"10.3233\/ICA-190608_ref29","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1109\/TVCG.2010.9","article-title":"Fast construction of k-nearest neighbor graphs for point clouds","volume":"16","author":"Connor","year":"2010","journal-title":"IEEE Transactions on Visualization and Computer Graphics."},{"key":"10.3233\/ICA-190608_ref30","unstructured":"Angelina Uy M, Hee Lee G. Pointnetvlad: Deep point cloud based retrieval for large-scale place recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2018. pp. 4470\u20134479."},{"key":"10.3233\/ICA-190608_ref31","doi-asserted-by":"crossref","unstructured":"Armeni I, Sener O, Zamir AR, Jiang H, Brilakis I, Fischer M, et al. 3d semantic parsing of large-scale indoor spaces. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2016. pp. 1534\u20131543.","DOI":"10.1109\/CVPR.2016.170"},{"issue":"1","key":"10.3233\/ICA-190608_ref32","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1007\/s11766-017-3450-3","article-title":"A sketch-based semantic retrieval approach for 3D CAD models","volume":"32","author":"Qin","year":"2017","journal-title":"Applied Mathematics-A Journal of Chinese Universities."},{"key":"10.3233\/ICA-190608_ref33","doi-asserted-by":"crossref","unstructured":"Sun J, Ovsjanikov M, Guibas L. A concise and provably informative multi-scale signature based on heat diffusion. In: Computer Graphics Forum. Vol. 28. Wiley Online Library; 2009. pp. 1383\u20131392.","DOI":"10.1111\/j.1467-8659.2009.01515.x"},{"key":"10.3233\/ICA-190608_ref34","doi-asserted-by":"crossref","unstructured":"Knopp J, Prasad M, Willems G, Timofte R, Van Gool L. Hough transform and 3D SURF for robust three dimensional classification. In: European Conference on Computer Vision. Springer; 2010. pp. 589\u2013602.","DOI":"10.1007\/978-3-642-15567-3_43"},{"key":"10.3233\/ICA-190608_ref35","doi-asserted-by":"crossref","unstructured":"Chen DY, Tian XP, Shen YT, Ouhyoung M. On visual similarity based 3D model retrieval. In: Computer Graphics Forum. Vol. 22. Wiley Online Library; 2003. pp. 223\u2013232.","DOI":"10.1111\/1467-8659.00669"},{"key":"10.3233\/ICA-190608_ref36","unstructured":"Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems; 2012. pp. 1097\u20131105."},{"key":"10.3233\/ICA-190608_ref37","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, et al. Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2015. pp. 1\u20139.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"10.3233\/ICA-190608_ref38","doi-asserted-by":"crossref","unstructured":"Xie S, Girshick R, Doll\u00e1r P, Tu Z, He K. Aggregated residual transformations for deep neural networks. In: Computer Vision and Pattern Recognition (CVPR), 2017 IEEE Conference on. IEEE; 2017. pp. 5987\u20135995.","DOI":"10.1109\/CVPR.2017.634"},{"key":"10.3233\/ICA-190608_ref39","doi-asserted-by":"crossref","unstructured":"Charles RQ, Su H, Mo K, Guibas LJ. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition; 2017. pp. 77\u201385.","DOI":"10.1109\/CVPR.2017.16"},{"key":"10.3233\/ICA-190608_ref40","unstructured":"Qi CR, Yi L, Su H, Guibas LJ. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. In: Advances in Neural Information Processing Systems; 2017. pp. 5099\u20135108."},{"key":"10.3233\/ICA-190608_ref41","unstructured":"Li Y, Bu R, Sun M, Wu W, Di X, Chen B. PointCNN: Convolution On X-Transformed Points. In: Advances in Neural Information Processing Systems; 2018. pp. 828\u2013838."},{"key":"10.3233\/ICA-190608_ref42","doi-asserted-by":"crossref","unstructured":"Klokov R, Lempitsky V. Escape from cells: Deep kd-networks for the recognition of 3d point cloud models. In: Computer Vision (ICCV), 2017 IEEE International Conference on. IEEE; 2017. pp. 863\u2013872.","DOI":"10.1109\/ICCV.2017.99"},{"key":"10.3233\/ICA-190608_ref43","unstructured":"Jaderberg M, Simonyan K, Zisserman A, et al. Spatial transformer networks. In: Advances in Neural Information Processing Systems; 2015. pp. 2017\u20132025."},{"key":"10.3233\/ICA-190608_ref44","doi-asserted-by":"crossref","unstructured":"J\u00e9gou H, Douze M, Schmid C, P\u00e9rez P. Aggregating local descriptors into a compact image representation. In: Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on. IEEE; 2010. pp. 3304\u20133311.","DOI":"10.1109\/CVPR.2010.5540039"},{"key":"10.3233\/ICA-190608_ref45","doi-asserted-by":"crossref","unstructured":"Arandjelovic R, Gronat P, Torii A, Pajdla T, Sivic J. NetVLAD: CNN architecture for weakly supervised place recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2016. pp. 5297\u20135307.","DOI":"10.1109\/CVPR.2016.572"},{"key":"10.3233\/ICA-190608_ref46","unstructured":"Cignoni P, Callieri M, Corsini M, Dellepiane M, Ganovelli F, Ranzuglia G. Meshlab: an open-source mesh processing tool. In: Eurographics Italian Chapter Conference. Vol. 2008; 2008. pp. 129\u2013136."},{"key":"10.3233\/ICA-190608_ref47","doi-asserted-by":"crossref","unstructured":"Te G, Hu W, Zheng A, Guo Z. RGCNN: Regularized Graph CNN for Point Cloud Segmentation. In: Proceedings of the 26th ACM International Conference on Multimedia. MM \u201918. New York, NY, USA: ACM; 2018. pp. 746\u2013754. Available from: http:\/\/doi.acm.org\/10.1145\/3240508.3240621.","DOI":"10.1145\/3240508.3240621"},{"key":"10.3233\/ICA-190608_ref48","doi-asserted-by":"crossref","unstructured":"Li J, Chen BM, Hee Lee G. So-net: Self-organizing network for point cloud analysis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2018. pp.\u00a09397\u20139406.","DOI":"10.1109\/CVPR.2018.00979"},{"key":"10.3233\/ICA-190608_ref49","doi-asserted-by":"crossref","unstructured":"Simonovsky M, Komodakis N. Dynamic edge-conditioned filters in convolutional neural networks on graphs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2017. pp. 3693\u20133702.","DOI":"10.1109\/CVPR.2017.11"},{"key":"10.3233\/ICA-190608_ref51","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.cag.2013.11.009","article-title":"Interactive shape co-segmentation via label propagation","volume":"38","author":"Wu","year":"2014","journal-title":"Computers & Graphics."},{"issue":"4","key":"10.3233\/ICA-190608_ref52","doi-asserted-by":"crossref","first-page":"3145","DOI":"10.1109\/LRA.2018.2850061","article-title":"3DmFV: three-dimensional point cloud classification in real-time using convolutional neural networks","volume":"3","author":"Ben-Shabat","year":"2018","journal-title":"IEEE Robotics and Automation Letters."},{"key":"10.3233\/ICA-190608_ref53","doi-asserted-by":"crossref","unstructured":"Tchapmi L, Choy C, Armeni I, Gwak J, Savarese S. Segcloud: Semantic segmentation of 3d point clouds. In: 2017 International Conference on 3D Vision (3DV). IEEE; 2017. pp. 537\u2013547.","DOI":"10.1109\/3DV.2017.00067"},{"key":"10.3233\/ICA-190608_ref54","doi-asserted-by":"crossref","unstructured":"Huang Q, Wang W, Neumann U. Recurrent slice networks for 3d segmentation of point clouds. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2018. pp. 2626\u20132635.","DOI":"10.1109\/CVPR.2018.00278"},{"key":"10.3233\/ICA-190608_ref55","doi-asserted-by":"crossref","unstructured":"Xie S, Liu S, Chen Z, Tu Z. Attentional ShapeContextNet for Point Cloud Recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2018. pp. 4606\u20134615.","DOI":"10.1109\/CVPR.2018.00484"},{"key":"10.3233\/ICA-190608_ref56","doi-asserted-by":"crossref","unstructured":"Ip CY, Regli WC, Sieger L, Shokoufandeh A. Automated learning of model classifications. In: Proceedings of the Eighth ACM Symposium on Solid Modeling and Applications. ACM; 2003. pp. 322\u2013327.","DOI":"10.1145\/781606.781659"},{"issue":"5","key":"10.3233\/ICA-190608_ref57","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1080\/16864360.2005.10738325","article-title":"Content-based classification of CAD models with supervised learning","volume":"2","author":"Yiu Ip","year":"2005","journal-title":"Computer-aided Design and Applications."},{"issue":"1-4","key":"10.3233\/ICA-190608_ref58","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1080\/16864360.2005.10738363","article-title":"SVM-based semantic clustering and retrieval of a 3D model database","volume":"2","author":"Hou","year":"2005","journal-title":"Computer-Aided Design and Applications."},{"issue":"3","key":"10.3233\/ICA-190608_ref59","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"Imagenet large scale visual recognition challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"International Journal of Computer Vision."},{"issue":"2","key":"10.3233\/ICA-190608_ref60","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1631\/jzus.C1300185","article-title":"A deep learning approach to the classification of 3D CAD models","volume":"15","author":"Qin","year":"2014","journal-title":"Journal of Zhejiang University SCIENCE C."},{"key":"10.3233\/ICA-190608_ref61","doi-asserted-by":"crossref","unstructured":"Eigen D, Fergus R. Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture. In: Proceedings of the IEEE International Conference on Computer Vision; 2015. pp. 2650\u20132658.","DOI":"10.1109\/ICCV.2015.304"},{"key":"10.3233\/ICA-190608_ref62","doi-asserted-by":"crossref","unstructured":"Gong Y, Wang L, Guo R, Lazebnik S. Multi-scale orderless pooling of deep convolutional activation features. In: European Conference on Computer Vision. Springer; 2014. pp.\u00a0392\u2013407.","DOI":"10.1007\/978-3-319-10584-0_26"},{"key":"10.3233\/ICA-190608_ref63","unstructured":"Csurka G, Dance C, Fan L, Willamowski J, Bray C. Visual categorization with bags of keypoints. In: Workshop on Statistical Learning in Computer Vision, ECCV. Vol. 1. Prague; 2004. pp. 1\u20132."},{"key":"10.3233\/ICA-190608_ref64","doi-asserted-by":"crossref","unstructured":"Engelcke M, Rao D, Wang DZ, Tong CH, Posner I. Vote3deep: Fast object detection in 3d point clouds using efficient convolutional neural networks. In: 2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE; 2017. pp. 1355\u20131361.","DOI":"10.1109\/ICRA.2017.7989161"},{"key":"10.3233\/ICA-190608_ref65","unstructured":"Li Y, Pirk S, Su H, Qi CR, Guibas LJ. Fpnn: Field probing neural networks for 3d data. In: Advances in Neural Information Processing Systems; 2016. pp. 307\u2013315."},{"key":"10.3233\/ICA-190608_ref66","doi-asserted-by":"crossref","unstructured":"Lenc K, Vedaldi A. Understanding image representations by measuring their equivariance and equivalence. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE; 2015. pp. 991\u2013999.","DOI":"10.1109\/CVPR.2015.7298701"}],"container-title":["Integrated Computer-Aided Engineering"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/ICA-190608","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:14:18Z","timestamp":1777454058000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/ICA-190608"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,27]]},"references-count":65,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.3233\/ica-190608","relation":{},"ISSN":["1069-2509","1875-8835"],"issn-type":[{"value":"1069-2509","type":"print"},{"value":"1875-8835","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,11,27]]}}}