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SHREC'12 Track: Generic 3D Shape Retrieval

Objective: The objective of this track is to evaluate the performance of 3D shape retrieval approaches on Generic 3D Dataset.

Introduction: With the increasing number of 3D models are created every day and stored in databases, effectively searching a 3D repository for 3D shapes which are similar to a given 3D query model has become an important area of research. Benchmarking allows researchers to evaluate the quality of the results of different 3D shape retrieval approaches.

Task description:
The task is to evaluate the dissimilarity between every two objects in the database mentioned above and then output the dissimilarity matrix.

Dataset:
All the 3D models in the generic 3D dataset will be based on the combination of models from our previous generic 3D benchmarks. In this generic 3D dataset, there will be 1200 3D models, classified into 60 object categories based mainly on visual similarity. The file format used to represent the 3D models will be the ASCII Object File Format (*.off).

Evaluation Methodology:
We will employ the following evaluation measures: Precision-Recall curve (PR), Nearest Neighbor (NN), First-Tier (FT), Second-Tier (ST), E-Measure (E), Discounted Cumulative Gain (DCG) and Average Precision (AP).

Please cite the paper:
B. Li, A. Godil, M. Aono, X. Bai, T. Furuya, L. Li, R. Lopez-Sastre, H. Johan, R. Ohbuchi, C. Redondo-Cabrera, A. Tatsuma, T. Yanagimachi, S. Zhang, In: M. Spagnuolo, M. Bronstein, A. Bronstein, and A. Ferreira (eds.): SHREC'12 Track: Generic 3D Shape Retrieval, Eurographics Workshop on 3D Object Retrieval 2012 (3DOR 2012), 2012. http://dx.doi.org/10.2312/3DOR/3DOR12/119-126

About this Dataset

Updated: 2024-02-22
Metadata Last Updated: 2012-01-30
Date Created: N/A
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3D Shape Retrieval
Dataset Owner: N/A

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Title SHREC'12 Track: Generic 3D Shape Retrieval
Description Objective: The objective of this track is to evaluate the performance of 3D shape retrieval approaches on Generic 3D Dataset. Introduction: With the increasing number of 3D models are created every day and stored in databases, effectively searching a 3D repository for 3D shapes which are similar to a given 3D query model has become an important area of research. Benchmarking allows researchers to evaluate the quality of the results of different 3D shape retrieval approaches. Task description: The task is to evaluate the dissimilarity between every two objects in the database mentioned above and then output the dissimilarity matrix. Dataset: All the 3D models in the generic 3D dataset will be based on the combination of models from our previous generic 3D benchmarks. In this generic 3D dataset, there will be 1200 3D models, classified into 60 object categories based mainly on visual similarity. The file format used to represent the 3D models will be the ASCII Object File Format (*.off). Evaluation Methodology: We will employ the following evaluation measures: Precision-Recall curve (PR), Nearest Neighbor (NN), First-Tier (FT), Second-Tier (ST), E-Measure (E), Discounted Cumulative Gain (DCG) and Average Precision (AP). Please cite the paper: B. Li, A. Godil, M. Aono, X. Bai, T. Furuya, L. Li, R. Lopez-Sastre, H. Johan, R. Ohbuchi, C. Redondo-Cabrera, A. Tatsuma, T. Yanagimachi, S. Zhang, In: M. Spagnuolo, M. Bronstein, A. Bronstein, and A. Ferreira (eds.): SHREC'12 Track: Generic 3D Shape Retrieval, Eurographics Workshop on 3D Object Retrieval 2012 (3DOR 2012), 2012. http://dx.doi.org/10.2312/3DOR/3DOR12/119-126
Modified N/A
Publisher Name National Institute of Standards and Technology
Contact mailto:afzal.godil@nist.gov
Keywords 3D Shape Retrieval , 3D Models , Evaluation and Measurement Science
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}

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