Monocular Image Based 3D Model Retrieval
Creators: name: Li, Wenhui
name: Liu, Anan
name: Nie, Weizhi
name: Song, Dan
name: Li, Yuqian
name: Wang, Weijie
name: Xiang, Shu
name: Zhou, Heyu
name: Bui, Ngoc-Minh
name: Cen, Yunchi
name: Chen, Zenian
name: Chung-Nguyen, Huy-Hoang
name: Diep, Gia-Han
name: Do, Trong-Le
name: Doubrovski, Eugeni L.
name: Duong, Anh-Duc
name: Geraedts, Jo M. P.
name: Guo, Haobin
name: Hoang, Trung-Hieu
name: Li, Yichen
name: Liu, Xing
name: Liu, Zishun
name: Luu, Duc-Tuan
name: Ma, Yunsheng
name: Nguyen, Vinh-Tiep
name: Nie, Jie
name: Ren, Tongwei
name: Tran, Mai-Khiem
name: Tran-Nguyen, Son-Thanh
name: Tran, Minh-Triet
name: Vu-Le, The-Anh
name: Wang, Charlie C. L.
name: Wang, Shijie
name: Wu, Gangshan
name: Yang, Caifei
name: Yuan, Meng
name: Zhai, Hao
name: Zhang, Ao
name: Zhang, Fan
name: Zhao, Sicheng
Publisher: name: The Eurographics Association
Year: 2019
Subjects: subject: H.3.3 [Computer Graphics]
subject: Information Systems
subject: FOS: Computer and information sciences
subject: Information Search and Retrieval

description: Monocular image based 3D object retrieval is a novel and challenging research topic in the field of 3D object retrieval. Given a RGB image captured in real world, it aims to search for relevant 3D objects from a dataset. To advance this promising research, we organize this SHREC track and build the first monocular image based 3D object retrieval benchmark by collecting 2D images from ImageNet and 3D objects from popular 3D datasets such as NTU, PSB, ModelNet40 and ShapeNet. The benchmark contains classified 21,000 2D images and 7,690 3D objects of 21 categories. This track attracted 9 groups from 4 countries and the submission of 20 runs. To have a comprehensive comparison, 7 commonly-used retrieval performance metrics have been used to evaluate their retrieval performance. The evaluation results show that the supervised cross domain learning get the superior retrieval performance (Best NN is 97.4 %) by bridging the domain gap with label information. However, there is still a big challenge for unsupervised cross domain learning (Best NN is 61.2%), which is more practical for the real application. Although we provided both view images and OBJ file for each 3D model, all the participants use the view images to represent the 3D model. One of the interesting work in the future is directly using the 3D information and 2D RGB information to solve the task of monocular Image based 3D model retrieval.
description: Eurographics Workshop on 3D Object Retrieval
description: SHREC Session 2
description: 103
description: 110
description: Wenhui Li, Anan Liu, Weizhi Nie, Dan Song, Yuqian Li, Weijie Wang, Shu Xiang, Heyu Zhou, Ngoc-Minh Bui, Yunchi Cen, Zenian Chen, Huy-Hoang Chung-Nguyen, Gia-Han Diep, Trong-Le Do, Eugeni L. Doubrovski, Anh-Duc Duong, Jo M. P. Geraedts, Haobin Guo, Trung-Hieu Hoang, Yichen Li, Xing Liu, Zishun Liu, Duc-Tuan Luu, Yunsheng Ma, Vinh-Tiep Nguyen, Jie Nie, Tongwei Ren, Mai-Khiem Tran, Son-Thanh Tran-Nguyen, Minh-Triet Tran, The-Anh Vu-Le, Charlie C. L. Wang, Shijie Wang, Gangshan Wu, Caifei Yang, Meng Yuan, Hao Zhai, Ao Zhang, Fan Zhang, and Sicheng Zhao
description: Categories and Subject Descriptors (according to ACM CCS): H.3.3 [Computer Graphics]: Information Systems-Information Search and Retrieval