3DOR 17

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RGB-D to CAD Retrieval with ObjectNN Dataset
(The Eurographics Association, 2017) 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; Ioannis Pratikakis and Florent Dupont and Maks Ovsjanikov
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.