{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T09:29:17Z","timestamp":1762507757828,"version":"build-2065373602"},"reference-count":25,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2019,5,23]],"date-time":"2019-05-23T00:00:00Z","timestamp":1558569600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The National Social Science Fund of China","award":["18BGL202"],"award-info":[{"award-number":["18BGL202"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Retrieving 3D models by adopting hand-drawn sketches to be the input has turned out to be a popular study topic. Most current methods are based on manually selected features and the best view produced for 3D model calculations. However, there are many problems with these methods such as distortion. For the purpose of dealing with such issues, this paper proposes a novel feature representation method to select the projection view and adapt the maxout network to the extended Siamese network architecture. In addition, the strategy is able to handle the over-fitting issue of convolutional neural networks (CNN) and mitigate the discrepancies between the 3D shape domain and the sketch. A pre-trained AlexNet was used to sketch the extract features. For 3D shapes, multiple 2D views were compiled into compact feature vectors using pre-trained multi-view CNNs. Then the Siamese convolutional neural networks were learnt for transforming the two domains\u2019 original characteristics into nonlinear feature space, which mitigated the domain discrepancy and kept the discriminations. Two large data sets were used for experiments, and the experimental results show that the method is superior to the prior art methods in accuracy.<\/jats:p>","DOI":"10.3390\/sym11050703","type":"journal-article","created":{"date-parts":[[2019,5,24]],"date-time":"2019-05-24T02:22:00Z","timestamp":1558664520000},"page":"703","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A Novel Sketch-Based Three-Dimensional Shape Retrieval Method Using Multi-View Convolutional Neural Network"],"prefix":"10.3390","volume":"11","author":[{"given":"Dianhui","family":"Mao","sequence":"first","affiliation":[{"name":"Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China"},{"name":"National Engineering Laboratory for Agri-product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhihao","family":"Hao","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China"},{"name":"Pattern Analysis and Machine Intelligence Group, Department of Computer and Information Science, University of Macau, Taipa, Macau 999078, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"76087","DOI":"10.1109\/ACCESS.2018.2882711","article-title":"Non-rigid 3d model retrieval based on quadruplet convolutional neural networks","volume":"6","author":"Zeng","year":"2018","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"60","DOI":"10.4018\/IJGHPC.2018070105","article-title":"Sketch-based 3d model retrieval using attributes","volume":"10","author":"Lei","year":"2018","journal-title":"Int. J. Grid High Perform. Comput. (IJGHPC)"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2921","DOI":"10.1007\/s11042-017-4446-y","article-title":"A new sketch-based 3d model retrieval method by using composite features","volume":"77","author":"Li","year":"2018","journal-title":"Multimed. Tools Appl."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.cviu.2013.11.008","article-title":"A comparison of methods for sketch-based 3d shape retrieval","volume":"119","author":"Bo","year":"2014","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_5","first-page":"1","article-title":"Sketch-based shape retrieval","volume":"31","author":"Eitz","year":"2012","journal-title":"ACM Trans. Graph."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3374","DOI":"10.1109\/TIP.2018.2817042","article-title":"Deep correlated holistic metric learning for sketch-based 3d shape retrieval","volume":"27","author":"Dai","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4707","DOI":"10.1007\/s11042-013-1831-z","article-title":"User-drawn sketch-based 3d object retrievalusing sparse coding","volume":"74","author":"Sang","year":"2015","journal-title":"Multimed. Tools Appl."},{"key":"ref_8","unstructured":"Wang, F., and Li, Y. (2015). Sketch-based 3d shape retrieval using convolutional neural net. Comput. Sci., 1875\u20131883."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1145\/588272.588279","article-title":"A search engine for 3D models","volume":"22","author":"Funkhouser","year":"2003","journal-title":"ACM Trans. Graph."},{"key":"ref_10","unstructured":"Funkhouser, T., and Shilane, P. (2006, January 26\u201328). Partial matching of 3D shapes with priority-driven search. Proceedings of the fourth Eurographics symposium on Geometry processing, Cagliari, Italy."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Eitz, M., Hildebrand, K., Boubekeur, T., and Alexa, M. (2010). Sketch-based 3d shape retrieval. ACM Trans. Graph.","DOI":"10.1145\/1837026.1837033"},{"key":"ref_12","unstructured":"Saavedra, J.M., Bustos, B., Schreck, T., Yoon, S., and Scherer, M. (2012). Sketch-Based 3d Model Retrieval Using Keyshapes for Global and Local Representation. Eurographics Workshop on 3D Object Retrieval, The Eurographics Association."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1016\/j.cag.2010.07.002","article-title":"An evaluation of descriptors for large-scale image retrieval from sketched feature lines","volume":"34","author":"Eitz","year":"2010","journal-title":"Comput. Graphi."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1624","DOI":"10.1109\/TVCG.2010.266","article-title":"Sketch-based image retrieval: Benchmark and bag-of-features descriptors","volume":"17","author":"Eitz","year":"2010","journal-title":"IEEE Trans. Vis. Comput. Graphi."},{"key":"ref_15","unstructured":"Li, B., Lu, Y., Godil, A., Schreck, T., Aono, M., Johan, H., Saavedra, J.M., and Tashiro, S. (2013, January 11). Shrec1\u20193 track: Large scale sketch-based 3d shape retrieval. Proceedings of the Eurographics Workshop on 3d Object Retrieval, Girona, Spain."},{"key":"ref_16","unstructured":"Li, B., Lu, Y., Li, C., Godil, A., Schreck, T., Aono, M., Burtscher, M., Fu, H., Furuya, T., and Johan, H. (2014, January 6). In Shrec\u201914 track: Extended large scale sketch-based 3d shape retrieval. Proceedings of the Eurographics Workshop on 3d Object Retrieval, Strasbourg, France."},{"key":"ref_17","first-page":"1","article-title":"How do humans sketch objects?","volume":"31","author":"Eitz","year":"2012","journal-title":"ACM. Trans. Graphi."},{"key":"ref_18","unstructured":"Shilane, P., Min, P., Kazhdan, M., and Funkhouser, T. (2004, January 7\u20139). The princeton shape benchmark. Proceedings of the Shape Modeling Applications, Genova, Italy."},{"key":"ref_19","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Dai, G., Xie, J., Zhu, F., and Fang, Y. (2017, January 4\u20136). Deep correlated metric learning for sketch-based 3d shape retrieval. Proceedings of the AAAI, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.11211"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"57","DOI":"10.3389\/fncom.2018.00057","article-title":"Perceptual dominance in brief presentations of mixed images: Human perception versus deep neural networks","volume":"12","author":"Gruber","year":"2018","journal-title":"Front. Comput. Neurosci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1007\/s11042-012-1009-0","article-title":"Sketch-based 3d model retrieval by incorporating 2d-3d alignment","volume":"65","author":"Li","year":"2013","journal-title":"Multimed. Tools Appl."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Tang, P., Wang, C., Wang, X., Liu, W., Zeng, W., and Wang, J. (2019). Object detection in videos by high quality object linking. IEEE Trans. Pattern Anal. Mach. Intell., 1.","DOI":"10.1109\/TPAMI.2019.2910529"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","article-title":"Backpropagation applied to handwritten zip code recognition","volume":"1","author":"Lecun","year":"2014","journal-title":"Neural Comput."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1109\/JSSC.2018.2880918","article-title":"CONV-SRAM: An Energy-Efficient SRAM With In-Memory Dot-Product Computation for Low-Power Convolutional Neural Networks","volume":"54","author":"Biswas","year":"2019","journal-title":"IEEE J. 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