SHREC 2020 Track: Extended Monocular Image Based 3D Model Retrieval
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Date
2020
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Monocular image based 3D object retrieval has attracted more and more attentions in the field of 3D object retrieval. However, the research of 3D object retrieval based on 2D image is still challenging, mainly because of the gap between data from different modalities. To further support this research, we extend the previous track SHREC19'MI3DOR to organize this track, and we construct the expanded monocular image based 3D object retrieval benchmark. Compared with SHREC19'MI3DOR, this benchmark adds 19 categories for both 2D images and 3D models to the original 21 categories, taking into account the lack of categories for practical applications. Two groups participated, proposed three kinds of supervised methods and submitted 20 runs in total, and 7 commonly-used criteria are used to evaluate the retrieval performance. The results show that supervised methods still achieve satisfying retrieval results (Best NN is 96.7% for 40 categories), which are comparable to the results of SHREC19'MI3DOR. In the future, unsupervised methods are encouraged to discover in monocular image based 3D model retrieval.
Description
@inproceedings{10.2312:3dor.20201163,
booktitle = {Eurographics Workshop on 3D Object Retrieval},
editor = {Schreck, Tobias and Theoharis, Theoharis and Pratikakis, Ioannis and Spagnuolo, Michela and Veltkamp, Remco C.},
title = {{SHREC 2020 Track: Extended Monocular Image Based 3D Model Retrieval}},
author = {Li, Wenhui and Song, Dan and Le, Shengjie and Wang, Dandan and Ren, Tongwei and Wu, Gangshan and Vu-Le, The-Anh and Hoang, Xuan-Nhat and Nguyen, E-Ro and Nguyen-Ho, Thang-Long and Nguyen, Hai-Dang and Do, Trong-Le and Liu, Anan and Tran, Minh-Triet and Nie, Weizhi and Zhang, Ting and Zhao, Xiaoqian and Ma, Mingsheng and Li, Yuqian and Zhou, Heyu and Zhang, Beibei},
year = {2020},
publisher = {The Eurographics Association},
ISSN = {1997-0471},
ISBN = {978-3-03868-126-7},
DOI = {10.2312/3dor.20201163}
}