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RGB-D to CAD Retrieval with ObjectNN Dataset

 
: Hua, Binh-Son; Truong, Quang-Trung; Tran, Minh-Khoi; Pham, Quang-Hieu; Kanezaki, Asako; Lee, Tang; Chiang, Hung Yueh; 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

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Volltext ()

Pratikakis, Ioannis (Ed.) ; European Association for Computer Graphics -EUROGRAPHICS-:
Eurographics 2017 Workshop on 3D Object Retrieval, EG 3DOR 2017
Goslar: Eurographics Association, 2017 (Eurographics Workshop and Symposia Proceedings Series)
ISBN: 978-3-03868-030-7
S.25-32
Workshop on 3D Object Retrieval (EG 3DOR) <10, 2017, Lyon>
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IGD ()
Computer vision; Scene analysis; Object recognition; Digitized Work; computer graphics (CG)

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.

: http://publica.fraunhofer.de/dokumente/N-464348.html