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.
About this Dataset
| Title | SHREC'14 Track: Extended Large Scale Sketch-Based 3D Shape Retrieval |
|---|---|
| Description | 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. |
| Modified | 2014-01-01 00:00:00 |
| Publisher Name | National Institute of Standards and Technology |
| Contact | mailto:afzal.godil@nist.gov |
| Keywords | 3D Shape Retrieval , 3D Models , Sketches , Evaluation |
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