@inproceedings{12c3b6c1d5fa4b988a87771093e9812a,
title = "SHREC'17: RgB-D to CAD retrieval with ObjectNN dataset",
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.",
author = "Hua, {Binh Son} and Truong, {Quang Trung} and Tran, {Minh Khoi} and Pham, {Quang Hieu} and Asako Kanezaki and Tang Lee and Chiang, {Hung Yueh} and Winston Hsu and Bo Li and Yijuan Lu and Henry Johan and Shoki Tashiro and Masaki Aono 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 Bao Truong and Do, {Minh N.} and Duong, {Anh Duc} and Yu, {Lap Fai} and Nguyen, {Duc Thanh} and Yeung, {Sai Kit}",
note = "Publisher Copyright: {\textcopyright} 2017 The Eurographics Association.; 10th Eurographics Workshop on 3D Object Retrieval, 3DOR 2017 ; Conference date: 23-04-2017 Through 24-04-2017",
year = "2017",
doi = "10.2312/3dor.20171048",
language = "English (US)",
series = "Eurographics Workshop on 3D Object Retrieval, EG 3DOR",
publisher = "Eurographics Association",
pages = "25--32",
editor = "Ioannis Pratikakis and Maks Ovsjanikov and Florent Dupont",
booktitle = "EG 3DOR 2017 - Eurographics 2017 Workshop on 3D Object Retrieval",
}