Browsing by Author "Nguyen, Hai-Dang"

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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.
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    SHREC 2020 Track: River Gravel Characterization
    (The Eurographics Association, 2020) Giachetti, Andrea; Biasotti, Silvia; Moscoso Thompson, Elia; Fraccarollo, Luigi; Nguyen, Quang; Nguyen, Hai-Dang; Tran, Minh-Triet; Arvanitis, Gerasimos; Romanelis, Ioannis; Fotis, Vlasis; Moustakas, Konstantinos; Tortorici, Claudio; Werghi, Naoufel; Berretti, Stefano; Schreck, Tobias and Theoharis, Theoharis and Pratikakis, Ioannis and Spagnuolo, Michela and Veltkamp, Remco C.
    The quantitative analysis of the distribution of the different types of sands, gravels and cobbles shaping river beds is a very important task performed by hydrologists to derive useful information on fluvial dynamics and related processes (e.g., hydraulic resistance, sediment transport and erosion, habitat suitability. As the methods currently employed in the practice to perform this evaluation are expensive and time-consuming, the development of fast and accurate methods able to provide a reasonable estimate of the gravel distribution based on images or 3D scanning data would be extremely useful to support hydrologists in their work. To evaluate the suitability of state-of-the-art geometry processing tool to estimate the distribution from digital surface data, we created, therefore, a dataset including real captures of riverbed mockups, designed a retrieval task on it and proposed them as a challenge of the 3D Shape Retrieval Contest (SHREC) 2020. In this paper, we discuss the results obtained by the methods proposed by the groups participating in the contest and baseline methods provided by the organizers. Retrieval methods have been compared using the precision-recall curves, nearest neighbor, first tier, second tier, normalized discounted cumulated gain and average dynamic recall. Results show the feasibility of gravels characterization from captured surfaces and issues in the discrimination of mixture of gravels of different size.
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    SHREC 2021: Surface-based Protein Domains Retrieval
    (The Eurographics Association, 2021) Langenfeld, Florent; Aderinwale, Tunde; Christoffer, Charles; Shin, Woong-Hee; Terashi, Genki; Wang, Xiao; Kihara, Daisuke; Benhabiles, Halim; Hammoudi, Karim; Cabani, Adnane; Windal, Feryal; Melkemi, Mahmoud; Otu, Ekpo; Zwiggelaar, Reyer; Hunter, David; Liu, Yonghuai; Sirugue, Léa; Nguyen, Huu-Nghia H.; Nguyen, Tuan-Duy H.; Nguyen–Truong, Vinh-Thuyen; Le, Danh; Nguyen, Hai-Dang; Tran, Minh-Triet; Montès, Matthieu; Biasotti, Silvia and Dyke, Roberto M. and Lai, Yukun and Rosin, Paul L. and Veltkamp, Remco C.
    Proteins are essential to nearly all cellular mechanism, and often interact through their surface with other cell molecules, such as proteins and ligands. The evolution generates plenty of different proteins, with unique abilities, but also proteins with related functions hence surface, which is therefore of primary importance for their activity. In the present work, we assess the ability of five methods to retrieve similar protein surfaces, using either their shape only (3D meshes), or their shape and the electrostatic potential at their surface, an important surface property. Five different groups participated in this challenge using the shape only, and one group extended its pre-existing algorithm to handle the electrostatic potential. The results reveal both the ability of the methods to detect related proteins and their difficulties to distinguish between topologically related proteins.