Browsing by Author "Tran, Minh-Triet"

Now showing 1 - 6 of 6
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    Extended 2D Scene Image-Based 3D Scene Retrieval
    (The Eurographics Association, 2019) Abdul-Rashid, Hameed; Yuan, Juefei; Li, Bo; Lu, Yijuan; Schreck, Tobias; Bui, Ngoc-Minh; Do, Trong-Le; Holenderski, Mike; Jarnikov, Dmitri; Le, Khiem T.; Menkovski, Vlado; Nguyen, Khac-Tuan; Nguyen, Thanh-An; Nguyen, Vinh-Tiep; Ninh, Tu V.; Rey, Perez; Tran, Minh-Triet; Wang, Tianyang; Biasotti, Silvia and Lavoué, Guillaume and Veltkamp, Remco
    In the months following our SHREC 2018 - 2D Scene Image-Based 3D Scene Retrieval (SceneIBR2018) track, we have extended the number of the scene categories from the initial 10 classes in the SceneIBR2018 benchmark to 30 classes, resulting in a new benchmark SceneIBR2019 which has 30,000 scene images and 3,000 3D scene models. For that reason, we seek to further evaluate the performance of existing and new 2D scene image-based 3D scene retrieval algorithms using this extended and more comprehensive new benchmark. Three groups from the Netherlands, the United States and Vietnam participated and collectively submitted eight runs. This report documents the evaluation of each method based on seven performance metrics, offers an indepth discussion as well as analysis on the methods employed and discusses future directions that have the potential to address this task. Again, deep learning techniques have demonstrated notable performance in terms of both accuracy and scalability when applied to this exigent retrieval task. To further enrich the current state of 3D scene understanding and retrieval, our evaluation toolkit, all participating methods' results and the comprehensive 2D/3D benchmark have all been made publicly available.
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    Extended 2D Scene Sketch-Based 3D Scene Retrieval
    (The Eurographics Association, 2019) Yuan, Juefei; Abdul-Rashid, Hameed; Li, Bo; Lu, Yijuan; Schreck, Tobias; Bui, Ngoc-Minh; Do, Trong-Le; Nguyen, Khac-Tuan; Nguyen, Thanh-An; Nguyen, Vinh-Tiep; Tran, Minh-Triet; Wang, Tianyang; Biasotti, Silvia and Lavoué, Guillaume and Veltkamp, Remco
    Sketch-based 3D scene retrieval is to retrieve 3D scene models given a user's hand-drawn 2D scene sketch. It is a brand new but also very challenging research topic in the field of 3D object retrieval due to the semantic gap in their representations: 3D scene models or views differ from non-realistic 2D scene sketches. To boost this interesting research, we organized a 2D Scene Sketch-Based 3D Scene Retrieval track in SHREC'18, resulting a SceneSBR18 benchmark which contains 10 scene classes. In order to make it more comprehensive, we have extended the number of the scene categories from the initial 10 classes in the SceneSBR2018 benchmark to 30 classes, resulting in a new and more challenging benchmark SceneSBR2019 which has 750 2D scene sketches and 3,000 3D scene models. Therefore, the objective of this track is to further evaluate the performance and scalability of different 2D scene sketch-based 3D scene model retrieval algorithms using this extended and more comprehensive new benchmark. In this track, two groups from USA and Vietnam have successfully submitted 4 runs. Based on 7 commonly used retrieval metrics, we evaluate their retrieval performance. We have also conducted a comprehensive analysis and discussion of these methods and proposed several future research directions to deal with this challenging research topic. Deep learning techniques have been proved their great potentials again in dealing with this challenging retrieval task, in terms of both retrieval accuracy and scalability to a larger dataset. We hope this publicly available benchmark, together with its evaluation results and source code, will further enrich and promote 2D scene sketch-based 3D scene retrieval research area and its corresponding applications.
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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.
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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.