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SHREC'17 Track: Point-Cloud Shape Retrieval of Non-Rigid Toys

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Limberger, F. A. | Wilson, R. C. | Aono, M. | Audebert, N. | Boulch, A. | Bustos, B. | Giachetti, A, A | Godil, A. | Le Saux, B. | Li, B. | Lu, Y. | Nguyen, H. D. | Nguyen, V. T. | Pham, V. K. | Sipiran, I. | Tatsuma, A | Tran, M. T. | Velasco-Forero, Santiago

Edité par CCSD -

International audience. 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 point-cloud shape retrieval problem. The database used in this task contains 100 3D point-cloud models which are classified into 10 different categories. All point clouds were generated by scanning each one of the models in their final poses using a 3D scanner, i.e., all models have been articulated before scanned. The retrieval performance is evaluated using seven commonly-used statistics (PR-plot, NN, FT, ST, E-measure, DCG, mAP). In total, there are 8 groups and 31 submissions taking part of this contest. The evaluation results shown by this work suggest that researchers are in the right way towards shape descriptors which can capture the main characteristics of 3D models, however, more tests still need to be made, since this is the first time we compare non-rigid signatures for point-cloud shape retrieval.

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