SHREC'17 track: Point-cloud shape retrieval of non-rigid toys

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

SHREC'17 track: Point-cloud shape retrieval of non-rigid toys

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

8 Scopus citations

Abstract

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.

Original language	Spanish
Title of host publication	Eurographics Workshop on 3D Object Retrieval, EG 3DOR
Pages	75-84
Number of pages	10
Volume	2017-April
State	Published - 1 Jan 2017
Externally published	Yes

Cite this

Limberger, F. A., Wilson, R. C., Aono, M., Audebert, N., Boulch, A., Bustos, B., Giachetti, 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, S. (2017). SHREC'17 track: Point-cloud shape retrieval of non-rigid toys. In Eurographics Workshop on 3D Object Retrieval, EG 3DOR (Vol. 2017-April, pp. 75-84)

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title = "SHREC'17 track: Point-cloud shape retrieval of non-rigid toys",

abstract = "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.",

author = "Limberger, {F. A.} and Wilson, {R. C.} and M. Aono and N. Audebert and A. Boulch and Benjamin Bustos and A. Giachetti and A. Godil and {Le Saux}, B. and B. Li and Y. Lu and Nguyen, {H. D.} and Nguyen, {V. T.} and Pham, {V. K.} and Ivan Sipiran and A. Tatsuma and Tran, {M. T.} and S. Velasco-Forero",

year = "2017",

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T1 - SHREC'17 track: Point-cloud shape retrieval of non-rigid toys

AU - Limberger, F. A.

AU - Wilson, R. C.

AU - Aono, M.

AU - Audebert, N.

AU - Boulch, A.

AU - Bustos, Benjamin

AU - Giachetti, A.

AU - Godil, A.

AU - Le Saux, B.

AU - Li, B.

AU - Lu, Y.

AU - Nguyen, H. D.

AU - Nguyen, V. T.

AU - Pham, V. K.

AU - Sipiran, Ivan

AU - Tatsuma, A.

AU - Tran, M. T.

AU - Velasco-Forero, S.

PY - 2017/1/1

Y1 - 2017/1/1

N2 - 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.

AB - 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.

M3 - Contribución a la conferencia

VL - 2017-April

SP - 75

EP - 84

BT - Eurographics Workshop on 3D Object Retrieval, EG 3DOR

ER -