https://scholars.lib.ntu.edu.tw/handle/123456789/413034
標題: | SHREC'17: RgB-D to CAD retrieval with ObjectNN dataset | 作者: | Hua B.-S. Truong Q.-T. Tran M.-K. Pham Q.-H. Kanezaki A. Lee T. Chiang H. Hsu W. Li B. Lu Y. Johan H. Tashiro S. Aono M. Tran M.-T. Pham V.-K. Nguyen H.-D. Nguyen V.-T. Tran Q.-T. Phan T.V. Truong B. Do M.N. Duong A.-D. Yu L.-F. Nguyen D.T. Yeung S.-K. |
公開日期: | 2017 | 卷: | 2017-April | 起(迄)頁: | 25-32 | 來源出版物: | Eurographics Workshop on 3D Object Retrieval, EG 3DOR | 摘要: | 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. ? 2017 The Eurographics Association. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/413034 | ISBN: | 9783038680307 | ISSN: | 19970463 | DOI: | 10.2312/3dor.20171048 |
顯示於: | 資訊工程學系 |
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