Browsing 3DOR 17 by Title

Now showing items 11-19 of 19

    • Point-Cloud Shape Retrieval of Non-Rigid Toys 

      Limberger, F. A.; Wilson, R. C.; Aono, M.; Audebert, N.; Boulch, A.; Bustos, B.; Giachetti, A.; Godil, A.; Saux, B. Le; Li, B.; Lu, Y.; Nguyen, H.-D.; Nguyen, V.-T.; Pham, V.-K.; Sipiran, I.; Tatsuma, A.; Tran, M.-T.; Velasco-Forero, S. (The Eurographics Association, 2017)
      In this paper, we present the results of the SHREC'17 Track: Point-Cloud Shape Retrieval of Non-Rigid Toys. The aim of this track is to create a fair benchmark to evaluate the performance of methods on the non-rigid ...

    • Protein Shape Retrieval 

      Song, Na; Craciun, Daniela; Christoffer, Charles W.; Han, Xusi; Kihara, Daisuke; Levieux, Guillaume; Montes, Matthieu; Qin, Hong; Sahu, Pranjal; Terashi, Genki; Liu, Haiguang (The Eurographics Association, 2017)
      The large number of protein structures deposited in the protein database provide an opportunity to examine the structure relations using computational algorithms, which can be used to classify the structures based on shape ...

    • Retrieval of Surfaces with Similar Relief Patterns 

      Biasotti, S.; Thompson, E. Moscoso; Aono, M.; Hamza, A. Ben; Bustos, B.; Dong, S.; Du, B.; Fehri, A.; Li, H.; Limberger, F. A.; Masoumi, M.; Rezaei, M.; Sipiran, I.; Sun, L.; Tatsuma, A.; Forero, S. Velasco; Wilson, R. C.; Wu, Y.; Zhang, J.; Zhao, T.; Fornasa, F.; Giachetti, A. (The Eurographics Association, 2017)
      This paper presents the results of the SHREC'17 contest on retrieval of surfaces with similar relief patterns. The proposed task was created in order to verify the possibility of retrieving surface patches with a relief ...

    • RGB-D to CAD Retrieval with ObjectNN Dataset 

      Hua, Binh-Son; Truong, Quang-Trung; Tran, Minh-Khoi; Pham, Quang-Hieu; Kanezaki, Asako; Lee, Tang; Chiang, HungYueh; Hsu, Winston; Li, Bo; Lu, Yijuan; Johan, Henry; Tashiro, Shoki; Aono, Masaki; Tran, Minh-Triet; Pham, Viet-Khoi; Nguyen, Hai-Dang; Nguyen, Vinh-Tiep; Tran, Quang-Thang; Phan, Thuyen V.; Truong, Bao; Do, Minh N.; Duong, Anh-Duc; Yu, Lap-Fai; Nguyen, Duc Thanh; Yeung, Sai-Kit (The Eurographics Association, 2017)
      The goal of this track is to study and evaluate the performance of 3D object retrieval algorithms using RGB-D data. This is inspired from the practical need to pair an object acquired from a consumer-grade depth camera to ...

    • Semantic Correspondence Across 3D Models for Example-based Modeling 

      Léon, Vincent; Itier, Vincent; Bonneel, Nicolas; Lavoué, Guillaume; Vandeborre, Jean-Philippe (The Eurographics Association, 2017)
      Modeling 3D shapes is a specialized skill not affordable to most novice artists due to its complexity and tediousness. At the same time, databases of complex models ready for use are becoming widespread, and can help the ...

    • Shape Similarity System driven by Digital Elevation Models for Non-rigid Shape Retrieval 

      Craciun, Daniela; Levieux, Guillaume; Montes, Matthieu (The Eurographics Association, 2017)
      Shape similarity computation is the main functionality for shape matching and shape retrieval systems. Existing shape similarity frameworks proceed by parameterizing shapes through the use of global and/or local representations ...

    • Sketch-based 3D Object Retrieval with Skeleton Line Views - Initial Results and Research Problems 

      Zhao, Xueqing; Gregor, Robert; Mavridis, Pavlos; Schreck, Tobias (The Eurographics Association, 2017)
      Hand-drawn sketches are a convenient way to define 3D object retrieval queries. Numerous methods have been proposed for sketch-based 3D object retrieval. Such methods employ a non-photo-realistic rendering step to create ...

    • Towards Recognizing of 3D Models Using A Single Image 

      Rashwan, Hatem A.; Chambon, Sylvie; Morin, Geraldine; Gurdjos, Pierre; Charvillat, Vincent (The Eurographics Association, 2017)
      As 3D data is getting more popular, techniques for retrieving a particular 3D model are necessary. We want to recognize a 3D model from a single photograph; as any user can easily get an image of a model he/she would like ...

    • Unstructured Point Cloud Semantic Labeling Using Deep Segmentation Networks 

      Boulch, Alexandre; Saux, Bertrand Le; Audebert, Nicolas (The Eurographics Association, 2017)
      In this work, we describe a new, general, and efficient method for unstructured point cloud labeling. As the question of efficiently using deep Convolutional Neural Networks (CNNs) on 3D data is still a pending issue, we ...