Browsing by Author "Liu, Anan"

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    Monocular Image Based 3D Model Retrieval
    (The Eurographics Association, 2019) Li, Wenhui; Liu, Anan; Nie, Weizhi; Song, Dan; Li, Yuqian; Wang, Weijie; Xiang, Shu; Zhou, Heyu; Bui, Ngoc-Minh; Cen, Yunchi; Chen, Zenian; Chung-Nguyen, Huy-Hoang; Diep, Gia-Han; Do, Trong-Le; Doubrovski, Eugeni L.; Duong, Anh-Duc; Geraedts, Jo M. P.; Guo, Haobin; Hoang, Trung-Hieu; Li, Yichen; Liu, Xing; Liu, Zishun; Luu, Duc-Tuan; Ma, Yunsheng; Nguyen, Vinh-Tiep; Nie, Jie; Ren, Tongwei; Tran, Mai-Khiem; Tran-Nguyen, Son-Thanh; Tran, Minh-Triet; Vu-Le, The-Anh; Wang, Charlie C. L.; Wang, Shijie; Wu, Gangshan; Yang, Caifei; Yuan, Meng; Zhai, Hao; Zhang, Ao; Zhang, Fan; Zhao, Sicheng; Biasotti, Silvia and Lavoué, Guillaume and Veltkamp, Remco
    Monocular image based 3D object retrieval is a novel and challenging research topic in the field of 3D object retrieval. Given a RGB image captured in real world, it aims to search for relevant 3D objects from a dataset. To advance this promising research, we organize this SHREC track and build the first monocular image based 3D object retrieval benchmark by collecting 2D images from ImageNet and 3D objects from popular 3D datasets such as NTU, PSB, ModelNet40 and ShapeNet. The benchmark contains classified 21,000 2D images and 7,690 3D objects of 21 categories. This track attracted 9 groups from 4 countries and the submission of 20 runs. To have a comprehensive comparison, 7 commonly-used retrieval performance metrics have been used to evaluate their retrieval performance. The evaluation results show that the supervised cross domain learning get the superior retrieval performance (Best NN is 97.4 %) by bridging the domain gap with label information. However, there is still a big challenge for unsupervised cross domain learning (Best NN is 61.2%), which is more practical for the real application. Although we provided both view images and OBJ file for each 3D model, all the participants use the view images to represent the 3D model. One of the interesting work in the future is directly using the 3D information and 2D RGB information to solve the task of monocular Image based 3D model retrieval.
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    SHREC 2020 Track: Extended Monocular Image Based 3D Model Retrieval
    (The Eurographics Association, 2020) Li, Wenhui; Song, Dan; Liu, Anan; Nie, Weizhi; Zhang, Ting; Zhao, Xiaoqian; Ma, Mingsheng; Li, Yuqian; Zhou, Heyu; Zhang, Beibei; Le, Shengjie; Wang, Dandan; Ren, Tongwei; Wu, Gangshan; Vu-Le, The-Anh; Hoang, Xuan-Nhat; Nguyen, E-Ro; Nguyen-Ho, Thang-Long; Nguyen, Hai-Dang; Do, Trong-Le; Tran, Minh-Triet; Schreck, Tobias and Theoharis, Theoharis and Pratikakis, Ioannis and Spagnuolo, Michela and Veltkamp, Remco C.
    Monocular image based 3D object retrieval has attracted more and more attentions in the field of 3D object retrieval. However, the research of 3D object retrieval based on 2D image is still challenging, mainly because of the gap between data from different modalities. To further support this research, we extend the previous track SHREC19'MI3DOR to organize this track, and we construct the expanded monocular image based 3D object retrieval benchmark. Compared with SHREC19'MI3DOR, this benchmark adds 19 categories for both 2D images and 3D models to the original 21 categories, taking into account the lack of categories for practical applications. Two groups participated, proposed three kinds of supervised methods and submitted 20 runs in total, and 7 commonly-used criteria are used to evaluate the retrieval performance. The results show that supervised methods still achieve satisfying retrieval results (Best NN is 96.7% for 40 categories), which are comparable to the results of SHREC19'MI3DOR. In the future, unsupervised methods are encouraged to discover in monocular image based 3D model retrieval.