RGB-D to CAD Retrieval with ObjectNN Dataset

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Date
2017
Authors
Hua, Binh-Son
Truong, Quang-Trung
Tran, Minh-Khoi
Pham, Quang-Hieu
Kanezaki, Asako
Lee, Tang
Chiang, HungYueh
Hsu, Winston
Li, Bo
Lu, Yijuan
Johan, Henry
Tashiro, Shoki
Aono, Masaki
Tran, Minh-Triet
Pham, Viet-Khoi
Nguyen, Hai-Dang
Nguyen, Vinh-Tiep
Tran, Quang-Thang
Phan, Thuyen V.
Truong, Bao
Do, Minh N.
Duong, Anh-Duc
Yu, Lap-Fai
Nguyen, Duc Thanh
Yeung, Sai-Kit
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
The goal of this track is to study and evaluate the performance of 3D object retrieval algorithms using RGB-D data. This is inspired from the practical need to pair an object acquired from a consumer-grade depth camera to CAD models available in public datasets on the Internet. To support the study, we propose ObjectNN, a new dataset with well segmented and annotated RGB-D objects from SceneNN [HPN 16] and CAD models from ShapeNet [CFG 15]. The evaluation results show that the RGB-D to CAD retrieval problem, while being challenging to solve due to partial and noisy 3D reconstruction, can be addressed to a good extent using deep learning techniques, particularly, convolutional neural networks trained by multi-view and 3D geometry. The best method in this track scores 82% in accuracy.
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@inproceedings{
10.2312:3dor.20171048
, booktitle = {
Eurographics Workshop on 3D Object Retrieval
}, editor = {
Ioannis Pratikakis and Florent Dupont and Maks Ovsjanikov
}, title = {{
RGB-D to CAD Retrieval with ObjectNN Dataset
}}, author = {
Hua, Binh-Son
and
Truong, Quang-Trung
and
Johan, Henry
and
Tashiro, Shoki
and
Aono, Masaki
and
Tran, Minh-Triet
and
Pham, Viet-Khoi
and
Nguyen, Hai-Dang
and
Nguyen, Vinh-Tiep
and
Tran, Quang-Thang
and
Phan, Thuyen V.
and
Truong, Bao
and
Tran, Minh-Khoi
and
Do, Minh N.
and
Duong, Anh-Duc
and
Yu, Lap-Fai
and
Nguyen, Duc Thanh
and
Yeung, Sai-Kit
and
Pham, Quang-Hieu
and
Kanezaki, Asako
and
Lee, Tang
and
Chiang, HungYueh
and
Hsu, Winston
and
Li, Bo
and
Lu, Yijuan
}, year = {
2017
}, publisher = {
The Eurographics Association
}, ISSN = {
1997-0471
}, ISBN = {
978-3-03868-030-7
}, DOI = {
10.2312/3dor.20171048
} }
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