{"type":"ScholarlyArticle","title":"Cross-Domain 3D Model Retrieval via Visual Domain Adaption","authors":[{"name":"Anan Liu","slug":"anan-liu","given":"Anan","family":"Liu","affiliation":"School of Electrical and Information Engineering , Tianjin University , China"},{"name":"Shu Xiang","slug":"shu-xiang","given":"Shu","family":"Xiang","affiliation":"School of Electrical and Information Engineering , Tianjin University , China"},{"name":"Wenhui Li","slug":"wenhui-li","given":"Wenhui","family":"Li","affiliation":"School of Electrical and Information Engineering , Tianjin University , China"},{"name":"Weizhi Nie","slug":"weizhi-nie","given":"Weizhi","family":"Nie","affiliation":"School of Electrical and Information Engineering , Tianjin University , China"},{"name":"Yuting Su","slug":"yuting-su","given":"Yuting","family":"Su","affiliation":"School of Electrical and Information Engineering , Tianjin University , China"}],"abstract":"Recent advances in 3D capturing devices and 3D\r\nmodeling software have led to extensive and diverse\r\n3D datasets, which usually have different distributions.\r\nCross-domain 3D model retrieval is becoming\r\nan important but challenging task. However,\r\nexisting works mainly focus on 3D model\r\nretrieval in a closed dataset, which seriously constrain\r\ntheir implementation for real applications. To\r\naddress this problem, we propose a novel crossdomain\r\n3D model retrieval method by visual domain\r\nadaptation. This method can inherit the advantage\r\nof deep learning to learn multi-view visual\r\nfeatures in the data-driven manner for 3D\r\nmodel representation. Moreover, it can reduce\r\nthe domain divergence by exploiting both domainshared\r\nand domain-specific features of different domains.\r\nConsequently, it can augment the discrimination\r\nof visual descriptors for cross-domain similarity\r\nmeasure. Extensive experiments on two popular\r\ndatasets, under three designed cross-domain\r\nscenarios, demonstrate the superiority and effectiveness\r\nof the proposed method by comparing\r\nagainst the state-of-the-art methods. Especially, the\r\nproposed method can significantly outperform the\r\nmost recent method for cross-domain 3D model retrieval\r\nand the champion of Shrec’16 Large-Scale\r\n3D Shape Retrieval from ShapeNet Core55.","doi":"10.24963/ijcai.2018/115","url":"https://ijcai.org/proceedings/2018/115","pdf":"https://ijcai.org/proceedings/2018/0115.pdf","markdown":"https://ijcai.org/proceedings/2018/115.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/115","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Computer Vision","track":"Main track","number":115,"pages":{"first":828,"last":834},"keywords":["Computer Vision: Recognition: Detection, Categorization, Indexing, Matching, Retrieval, Semantic Interpretation","Machine Learning: Transfer, Adaptation, Multi-task Learning"],"videos":[],"references":[{"position":1,"raw":"[Ansary et al., 2007] Tarik Filali Ansary, Mohamed Daoudi, and Jean-Philippe Vandeborre. A bayesian 3-d search en- gine using adaptive views clustering. IEEE Trans. Multi- media, 9(1):78-88, 2007.","title":"A bayesian 3-d search engine using adaptive views clustering","authors":"Tarik Filali Ansary, Mohamed Daoudi, Jean-Philippe Vandeborre","year":"2007","venue":"IEEE Trans. Multimedia","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Chang et al., 2015] Angel X. Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, Jianxiong Xiao, Li Yi, and Fisher Yu. ShapeNet: An Information-Rich 3D Model Repository. Technical Report arXiv:1512.03012 [cs.GR, Stanford University- Princeton University-Toyota Technological Institute at Chicago, 2015.","title":"ShapeNet: An Information-Rich 3D Model Repository","authors":"X Angel, Thomas Chang, Leonidas Funkhouser, Pat Guibas, Qixing Hanrahan, Zimo Huang, Silvio Li, Manolis Savarese, Shuran Savva, Hao Song, Jianxiong Su, Li Xiao, Fisher Yi, Yu","year":"2015","venue":null,"doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Chen et al., 2003] Ding-Yun Chen, Xiao-Pei Tian, Yu-Te Shen, and Ming Ouhyoung. On visual similarity based 3d model retrieval. Comput. Graph. Forum, 22(3):223-232, 2003.","title":"On visual similarity based 3d model retrieval","authors":"Ding-Yun Chen, Xiao-Pei Tian, Yu-Te Shen, Ming Ouhyoung","year":"2003","venue":"Comput. Graph. Forum","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Cheng et al., 2017] Zhiyong Cheng, Jialie Shen, Liqiang Nie, Tat-Seng Chua, and Mohan S. Kankanhalli. Explor- ing user-specific information in music retrieval. In ACM SIGIR, pages 655-664, 2017.","title":"Kankanhalli. Exploring user-specific information in music retrieval","authors":"Zhiyong Cheng, Jialie Shen, Liqiang Nie, Tat-Seng Chua, Mohan S","year":"2017","venue":"ACM SIGIR","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Gao and Dai, 2014] Yue Gao and Qionghai Dai. View- based 3d object retrieval: Challenges and approaches. IEEE MultiMedia, 21(3):52-57, 2014.","title":"Viewbased 3d object retrieval: Challenges and approaches","authors":"Dai Gao, Yue Gao, Qionghai Dai","year":"2014","venue":"IEEE MultiMedia","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Gao et al., 2011] Yue Gao, Qionghai Dai, Meng Wang, and Naiyao Zhang. 3d model retrieval using weighted bipartite graph matching. Sig. Proc.: Image Comm., 26(1):39-47, 2011.","title":"3d model retrieval using weighted bipartite graph matching","authors":"Yue Gao, Qionghai Dai, Meng Wang, Naiyao Zhang","year":"2011","venue":"Sig. Proc.: Image Comm","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Gao et al., 2012] Yue Gao, Meng Wang, Dacheng Tao, Rongrong Ji, and Qionghai Dai. 3-d object retrieval and recognition with hypergraph analysis. IEEE Trans. Image Processing, 21(9):4290-4303, 2012.","title":"3-d object retrieval and recognition with hypergraph analysis","authors":"Yue Gao, Meng Wang, Dacheng Tao, Rongrong Ji, Qionghai Dai","year":"2012","venue":"IEEE Trans. Image Processing","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Guo et al., 2015] Haiyun Guo, Jinqiao Wang, Min Xu, Zheng-Jun Zha, and Hanqing Lu. Learning multi-view deep features for small object retrieval in surveillance sce- narios. In ACM MM, pages 859-862, 2015.","title":"Learning multi-view deep features for small object retrieval in surveillance scenarios","authors":"Haiyun Guo, Jinqiao Wang, Min Xu, Zheng-Jun Zha, Hanqing Lu","year":"2015","venue":"ACM MM","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[He et al., 2017] Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. Neural collabo- rative filtering. In WWW, pages 173-182, 2017.","title":"Neural collaborative filtering","authors":"He","year":"2017","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Hong et al., 2016] Richang Hong, Zhenzhen Hu, Ruxin Wang, Meng Wang, and Dacheng Tao. Multi-view object retrieval via multi-scale topic models. IEEE Trans. Image Processing, 25(12):5814-5827, 2016.","title":"Multi-view object retrieval via multi-scale topic models","authors":"Hong","year":"2016","venue":"IEEE Trans. Image Processing","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Kalogerakis et al., 2016] Evangelos Kalogerakis, Melinos Averkiou, Subhransu Maji, and Siddhartha Chaudhuri. 3d shape segmentation with projective convolutional net- works. CoRR, abs/1612.02808, 2016.","title":"3d shape segmentation with projective convolutional networks","authors":"Evangelos Kalogerakis, Melinos Averkiou, Subhransu Maji, Siddhartha Chaudhuri","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Kanezaki, 2016] Asako Kanezaki. Rotationnet: Learning object classification using unsupervised viewpoint estima- tion. CoRR, abs/1603.06208, 2016.","title":"Rotationnet: Learning object classification using unsupervised viewpoint estimation","authors":"Asako Kanezaki","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Li et al., 2014] Wen Li, Lixin Duan, Dong Xu, and Ivor W. Tsang. Learning with augmented features for supervised and semi-supervised heterogeneous domain adaptation. IEEE Trans. Pattern Anal. Mach. Intell., 36(6):1134-1148, 2014.","title":"Learning with augmented features for supervised and semi-supervised heterogeneous domain adaptation","authors":"Wen Li, Lixin Duan, Dong Xu, Ivor W Tsang","year":"2014","venue":"IEEE Trans. Pattern Anal. Mach. Intell","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Li et al., 2016] Yangyan Li, Sören Pirk, Hao Su, Charles Ruizhongtai Qi, and Leonidas J. Guibas. FPNN: field probing neural networks for 3d data. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, pages 307-315, 2016.","title":"FPNN: field probing neural networks for 3d data","authors":"Li","year":"2016","venue":"Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Liu et al., 2017] Anan Liu, Weizhi Nie, Yue Gao, and Yut- ing Su. View-based 3-d model retrieval: A benchmark. IEEE Transactions on Cybernetics, PP(99):1-13, 2017.","title":"View-based 3-d model retrieval: A benchmark","authors":"Anan Liu, Weizhi Nie, Yue Gao, Yuting Su","year":"2017","venue":"IEEE Transactions on Cybernetics","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Long et al., 2013] Mingsheng Long, Jianmin Wang, Guiguang Ding, Jiaguang Sun, and Philip S. Yu. Transfer feature learning with joint distribution adaptation. In ICCV, pages 2200-2207, 2013.","title":"Transfer feature learning with joint distribution adaptation","authors":"Mingsheng Long, Jianmin Wang, Guiguang Ding, Jiaguang Sun, Philip S Yu","year":"2013","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Maturana and Scherer, 2015] Daniel Maturana and Sebas- tian Scherer. Voxnet: A 3d convolutional neural network for real-time object recognition. In IROS, pages 922-928, 2015.","title":"Voxnet: A 3d convolutional neural network for real-time object recognition","authors":"Scherer Maturana, Daniel Maturana, Sebastian Scherer","year":"2015","venue":"IROS","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Nie et al., 2016] Liqiang Nie, Xuemeng Song, and Tat-Seng Chua. Learning from Multiple Social Networks. Synthe- sis Lectures on Information Concepts, Retrieval, and Ser- vices. Morgan & Claypool Publishers, 2016.","title":"Learning from Multiple Social Networks","authors":"Liqiang Nie, Xuemeng Song, Tat-Seng Chua","year":"2016","venue":"Synthesis Lectures on Information Concepts, Retrieval, and Services","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Ohbuchi et al., 2008] Ryutarou Ohbuchi, Kunio Osada, Takahiko Furuya, and Tomohisa Banno. Salient local vi- sual features for shape-based 3d model retrieval. In 2008 International Conference on Shape Modeling and Appli- cations, pages 93-102, 2008.","title":"Salient local visual features for shape-based 3d model retrieval","authors":"Ohbuchi","year":"2008","venue":"2008 International Conference on Shape Modeling and Applications","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Pan et al., 2011] Sinno Jialin Pan, Ivor W. Tsang, James T. Kwok, and Qiang Yang. Domain adaptation via trans- fer component analysis. IEEE Trans. Neural Networks, 22(2):199-210, 2011.","title":"Domain adaptation via transfer component analysis","authors":"Ivor W Sinno Jialin Pan, James T Tsang, Qiang Kwok, Yang","year":"2011","venue":"IEEE Trans. Neural Networks","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Phong, 1975] Bui Tuong Phong. Illumination for computer generated pictures. Commun. ACM, 18(6):311-317, 1975.","title":"Illumination for computer generated pictures","authors":"Phong Bui Tuong","year":"1975","venue":"Commun. ACM","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Shih et al., 2007] Jau-Ling Shih, Chang-Hsing Lee, and Jian Tang Wang. A new 3d model retrieval approach based on the elevation descriptor. Pattern Recognition, 40(1):283-295, 2007.","title":"A new 3d model retrieval approach based on the elevation descriptor","authors":"Jau-Ling Shih, Chang-Hsing Lee, Jian Tang, Wang","year":"2007","venue":"Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Shilane et al., 2004] Philip Shilane, Patrick Min, Michael M. Kazhdan, and Thomas A. Funkhouser. The princeton shape benchmark. In SMI, pages 167-178, 2004.","title":"The princeton shape benchmark","authors":"Philip Shilane, Patrick Min, Michael M Kazhdan, Thomas A Funkhouser","year":"2004","venue":"SMI","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Su et al., 2015] Hang Su, Subhransu Maji, Evangelos Kalogerakis, and Erik G. Learned-Miller. Multi-view con- volutional neural networks for 3d shape recognition. In ICCV, pages 945-953, 2015.","title":"Learned-Miller. Multi-view convolutional neural networks for 3d shape recognition","authors":"Su","year":"2015","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Tang et al., 2017] Sheng Tang, Yu Li, Lixi Deng, and Yong- dong Zhang. Object localization based on proposal fusion. IEEE Trans. Multimedia, 19(9):2105-2116, 2017.","title":"Object localization based on proposal fusion","authors":"Sheng Tang, Yu Li, Lixi Deng, Yongdong Zhang","year":"2017","venue":"IEEE Trans. Multimedia","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Vranic, 2003] Dejan V. Vranic. An improvement of rota- tion invariant 3d-shape based on functions on concentric spheres. In ICIP, pages 757-760, 2003.","title":"An improvement of rotation invariant 3d-shape based on functions on concentric spheres","authors":"V Dejan, Vranic","year":"2003","venue":"ICIP","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Wu et al., 2015] Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, and Jianxiong Xiao. 3d shapenets: A deep representation for volumetric shapes. In CVPR, pages 1912-1920, 2015.","title":"3d shapenets: A deep representation for volumetric shapes","authors":"Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, Jianxiong Xiao","year":"2015","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Zhang et al., 2015] Kun Zhang, Mingming Gong, and Bern- hard Schölkopf. Multi-source domain adaptation: A causal view. In AAAI, pages 3150-3157, 2015.","title":"Multi-source domain adaptation: A causal view","authors":"Kun Zhang, Mingming Gong, Bernhard Schölkopf","year":"2015","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Zhang et al., 2017] Jing Zhang, Wanqing Li, and Philip Ogunbona. Joint geometrical and statistical alignment for visual domain adaptation. In CVPR, pages 5150-5158, 2017.","title":"Joint geometrical and statistical alignment for visual domain adaptation","authors":"Jing Zhang, Wanqing Li, Philip Ogunbona","year":"2017","venue":"CVPR","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}