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

Data and Resources

Field Value
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identifier ark:/88434/mds2-2221
issued 2020-04-22
landingPage https://data.nist.gov/od/id/mds2-2221
language {en}
license https://www.nist.gov/open/license
modified 2012-01-30 00:00:00
programCode {006:045}
publisher National Institute of Standards and Technology
references {http://dx.doi.org/10.2312/3DOR/3DOR12/119-126}
resource-type Dataset
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Groups
  • AmeriGEOSS
  • National Provider
  • North America
Tags
  • 3d-models
  • 3d-shape-retrieval
  • amerigeo
  • amerigeoss
  • ckan
  • evaluation-and-measurement-science
  • geo
  • geoss
  • national
  • north-america
  • united-states
isopen False
license_id other-license-specified
license_title other-license-specified
maintainer Afzal A. Godil
maintainer_email afzal.godil@nist.gov
metadata_created 2025-11-20T23:13:26.137663
metadata_modified 2025-11-20T23:13:26.137667
notes 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
num_resources 3
num_tags 11
title SHREC'12 Track: Generic 3D Shape Retrieval