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In this paper we report the results of the SHREC 2016 contest on "Retrieval of human subjects from depth sensor data". The proposed task was created in order to verify the possibility of retrieving models of query human subjects from single shots of depth sensors, using shape information only. Depth acquisition of different subjects were realized under different illumination conditions, using different clothes and in three different poses. The resulting point clouds of the partial body shape acquisitions were segmented and coupled with the skeleton provided by the OpenNI software and provided to the participants together with derived triangulated meshes. No color information was provided. Retrieval scores of the different methods proposed were estimated on the submitted dissimilarity matrices and the influence of the different acquisition conditions on the algorithms were also analyzed. Results obtained by the participants and by the baseline methods demonstrated that the proposed task is, as expected, quite difficult, especially due the partiality of the shape information and the poor accuracy of the estimated skeleton, but give useful insights on potential strategies that can be applied in similar retrieval procedures and derived practical applications.

Table of contents conference proceedings

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Shape Retrieval and 3D Gestural Interaction
Giachetti, Andrea / Caputo, Fabio Marco / Carcangiu, Alessandro / Scateni, Riccardo / Spano, Lucio Davide | 2016
Towards an Observer-oriented Theory of Shape Comparison
Frosini, Patrizio | 2016
3D Objects Exploration: Guidelines for Future Research
Biasotti, Silvia / Falcidieno, Bianca / Giorgi, Daniela / Spagnuolo, Michela | 2016
A Descriptor for Voxel Shapes Based on the Skeleton Cut Space
Feng, Cong / Jalba, Andrei C. / Telea, Alexandru C. | 2016
An Experimental Shape Matching Approach for Protein Docking
Fernandes, Francisco / Ferreira, Alfredo | 2016
An Edit Distance for Reeb Graphs
Bauer, Ulrich / Fabio, Barbara Di / Landi, Claudia | 2016
An Evaluation of Local Feature Encodings for Shape Retrieval
Tasse, Flora Ponjou / Kosinka, Jiri / Dodgson, Neil A. | 2016
Retrieval of Human Subjects from Depth Sensor Data
Giachetti, Andrea / Fornasa, Francesco / Parezzan, Federico / Saletti, Alessandro / Zambaldo, Leonardo / Zanini, Luisa / Achilles, Felix / Ichim, Alexandru-Eugen / Tombari, Federico / Navab, Nassir et al. | 2016
3D Sketch-Based 3D Shape Retrieval
Li, Bo / Lu, Yijuan / Duan, Fuqing / Dong, Shuilong / Fan, Yachun / Qian, Lu / Laga, Hamid / Li, Haisheng / Li, Yuxiang / Liu, Peng et al. | 2016
Matching of Deformable Shapes with Topological Noise
Lähner, Zorah / Rodolà, Emanuele / Bronstein, Michael M. / Cremers, Daniel / Burghard, Oliver / Cosmo, Luca / Dieckmann, Alexander / Klein, Reinhard / Sahillioğlu, Yusuf | 2016
Partial Matching of Deformable Shapes
Cosmo, Luca / Rodolà, Emanuele / Bronstein, Michael M. / Torsello, Andrea / Cremers, Daniel / Sahillioğlu, Yusuf | 2016
Shape Retrieval of Low-Cost RGB-D Captures
Pascoal, Pedro B. / Proença, Pedro / Gaspar, Filipe / Dias, Miguel Sales / Ferreira, Alfredo / Tatsuma, Atsushi / Aono, Masaki / Logoglu, K. Berker / Kalkan, Sinan / Temizel, Alptekin et al. | 2016
Partial Shape Queries for 3D Object Retrieval
Pratikakis, Ioannis / Savelonas, Michalis A. / Arnaoutoglou, Fotis / Ioannakis, George / Koutsoudis, Anestis / Theoharis, Theoharis / Tran, Minh-Triet / Nguyen, Vinh-Tiep / Pham, V.-K. / Nguyen, Hai-Dang et al. | 2016
Large-Scale 3D Shape Retrieval from ShapeNet Core55
Savva, Manolis / Yu, Fisher / Su, Hao / Aono, Masaki / Chen, Baoquan / Cohen-Or, Daniel / Deng, Weihong / Su, Hang / Bai, Song / Bai, Xiang et al. | 2016
3D Object Retrieval with Multimodal Views
Gao, Yue / Nie, Weizhi / Liu, Anan / Su, Yuting / Dai, Qionghai / An, Le / Chen, Fuhai / Cao, Liujuan / Dong, Shuilong / De, Yu et al. | 2016