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SHREC'14 Track: Extended Large Scale Sketch-Based 3D Shape Retrieval

Metadata Updated: July 29, 2022

The objective of SHREC'14 Track is to evaluate the performances of different sketch-based 3D model retrieval algorithms using a large scale hand-drawn sketch query dataset on a generic 3D model dataset.

Sketch-based 3D model retrieval is to retrieve relevant 3D models using sketch(es) as input. This scheme is intuitive and convenient for users to learn and search for 3D models. It is also popular and important for related applications such as sketch-based modeling and recognition, as well as 3D animation production via 3D reconstruction of a scene of 2D storyboard. However, most existing 3D model retrieval algorithms target the Query-by-Model framework which uses existing 3D models as queries.

Much less research work has been done regarding the Query-by-Sketch framework. Large scale sketch-based 3D shape retrieval has received more and more attentions in the community of content-based 3D object retrieval. The objective of this track is to evaluate the performance of different sketch-based 3D model retrieval algorithms using a large scale hand-drawn sketch query dataset on a comprehensive 3D model dataset. The benchmark contains 12,680 sketches and 8,987 3D models, divided into 171 distinct classes. In this track, 12 runs were submitted by 4 groups and their retrieval performance was evaluated using 7 commonly used retrieval performance metrics. We hope that this benchmark, the comparative evaluation results and the corresponding evaluation code will further promote the progress of this research direction for the 3D model retrieval community.

Evaluation Method: The performance is evaluated by Precision-Recall (PR) graph, Nearest Neighbor (NN), First Tier (FT), Second Tier (ST), E-Measures (E), Discounted Cumulated Gain (DCG) and Average Precision (AP)

Please cite the papers:

[1] Bo Li, Yijuan Lu, Chunyuan Li, Afzal Godil, Tobias Schreck, Masaki Aono, Martin Burtscher, Qiang Chen, Nihad Karim Chowdhury, Bin Fang, Hongbo Fu, Takahiko Furuya, Haisheng Li, Jianzhuang Liu, Henry Johan, Ryuichi Kosaka, Hitoshi Koyanagi, Ryutarou Ohbuchi, Atsushi Tatsuma, Yajuan Wan, Chaoli Zhang, Changqing Zou. A Comparison of 3D Shape Retrieval Methods Based on a Large-scale Benchmark Supporting Multimodal Queries. Computer Vision and Image Understanding, November 4, 2014.

[2] Bo Li, Yijuan Lu, Chunyuan Li, Afzal Godil, Tobias Schreck, Masaki Aono, Martin Burtscher, Hongbo Fu, Takahiko Furuya, Henry Johan, Jianzhuang Liu, Ryutarou Ohbuchi, Atsushi Tatsuma, Changqing Zou. SHREC' 14 Track: Extended Large Scale Sketch-Based 3D Shape Retrieval. Eurographics Workshop on 3D Object Retrieval 2014 (3DOR 2014): 121-130, 2014.

Access & Use Information

Public: This dataset is intended for public access and use. License: See this page for license information.

Downloads & Resources

References

https://doi.org/10.1016/j.cviu.2014.10.006
https://doi.org/10.2312/3dor.20141058

Dates

Metadata Created Date March 11, 2021
Metadata Updated Date July 29, 2022
Data Update Frequency irregular

Metadata Source

Harvested from NIST

Additional Metadata

Resource Type Dataset
Metadata Created Date March 11, 2021
Metadata Updated Date July 29, 2022
Publisher National Institute of Standards and Technology
Maintainer
Identifier ark:/88434/mds2-2218
Data First Published 2020-04-14
Language en
Data Last Modified 2014-01-01 00:00:00
Category Information Technology:Data and informatics, Mathematics and Statistics:Image and signal processing
Public Access Level public
Data Update Frequency irregular
Bureau Code 006:55
Metadata Context https://project-open-data.cio.gov/v1.1/schema/data.json
Schema Version https://project-open-data.cio.gov/v1.1/schema
Catalog Describedby https://project-open-data.cio.gov/v1.1/schema/catalog.json
Harvest Object Id 03368d3e-e5fd-4a1c-acc4-7134126e20fb
Harvest Source Id 74e175d9-66b3-4323-ac98-e2a90eeb93c0
Harvest Source Title NIST
Homepage URL https://data.nist.gov/od/id/mds2-2218
License https://www.nist.gov/open/license
Program Code 006:045
Related Documents https://doi.org/10.1016/j.cviu.2014.10.006, https://doi.org/10.2312/3dor.20141058
Source Datajson Identifier True
Source Hash 963beabde57f48b835d26bca0ed36d2f09273cbb
Source Schema Version 1.1

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