3DOR: Eurographics Workshop on 3D Object Retrieval

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Browsing 3DOR: Eurographics Workshop on 3D Object Retrieval by Subject "Abstracting methods"

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    3D Object Retrieval with Multimodal Views
    (The Eurographics Association, 2016) Gao, Yue; Nie, Weizhi; Liu, Anan; Su, Yuting; Dai, Qionghai; An, Le; Chen, Fuhai; Cao, Liujuan; Dong, Shuilong; De, Yu; Gao, Zan; Hao, Jiayun; Ji, Rongrong; Li, Haisheng; Liu, Mingxia; Pan, Lili; Qiu, Yu; Wei, Liwei; Wang, Zhao; Wei, Hongjiang; Zhang, Yuyao; Zhang, Jun; Zhang, Yang; Zheng, Yali; A. Ferreira and A. Giachetti and D. Giorgi
    This paper reports the results of the SHREC'16 track: 3D Object Retrieval with Multimodal Views, whose goal is to evaluate the performance of retrieval algorithms when multimodal views are employed for 3D object representation. In this task, a collection of 605 objects is generated and both the color images and the depth images are provided for each object. 200 objects including 100 3D printing models and 100 3D real objects are selected as the queries while the other 405 objects are selected as the tests and average retrieval performance is measured. The track attracted seven participants and the submission of 9 runs. Comparing to the last year's results, 3D printing models obviously introduce a bad influence. The performance of this year is worse than that of last year. This condition also shows a promising scenario about multimodal view-based 3D retrieval methods, and reveal interesting insights in dealing with multimodal data.
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    Retrieval and Classification on Textured 3D Models
    (The Eurographics Association, 2014) Biasotti, S.; Cerri, A.; Abdelrahman, M.; Aono, M.; Hamza, A. Ben; El-Melegy, M.; Farag, A.; Garro, V.; Giachetti, A.; Giorgi, D.; Godil, A.; Li, C.; Liu, Y.-J.; Martono, H. Y.; Sanada, C.; Tatsuma, A.; Velasco-Forero, S.; Xu, C.-X.; Benjamin Bustos and Hedi Tabia and Jean-Philippe Vandeborre and Remco Veltkamp
    This paper reports the results of the SHREC'14 track: Retrieval and classification on textured 3D models, whose goal is to evaluate the performance of retrieval algorithms when models vary either by geometric shape or texture, or both. The collection to search in is made of 572 textured mesh models, having a two-level classification based on geometry and texture. Together with the dataset, a training set of 96 models was provided. The track saw eight participants and the submission of 22 runs, to either the retrieval or the classification contest, or both. The evaluation results show a promising scenario about textured 3D retrieval methods, and reveal interesting insights in dealing with texture information in the CIELab rather than in the RGB colour space.
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    SHREC'13 Track: Retrieval on Textured 3D Models
    (The Eurographics Association, 2013) Cerri, A.; Biasotti, S.; Abdelrahman, M.; Angulo, J.; Berger, K.; Chevallier, L.; El-Melegy, M.; Farag, A.; Lefebvre, F.; Giachetti, A.; Guermoud, H.; Liu, Y.-J.; Velasco-Forero, S.; Vigouroux, JR.; Xu, C.-X.; Zhang, J.-B.; Umberto Castellani and Tobias Schreck and Silvia Biasotti and Ioannis Pratikakis and Afzal Godil and Remco Veltkamp
    This contribution reports the results of the SHREC 2013 track: Retrieval on Textured 3D Models, whose goal is to evaluate the performance of retrieval algorithms when models vary either by geometric shape or texture, or both. The collection to search in is made of 240 textured mesh models, divided into 10 classes. Each model has been used in turn as a query against the remaining part of the database. For a given query, the goal was to retrieve the most similar objects. The track saw six participants and the submission of eleven runs.