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北京航空航天大学 [1]

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	CurveNet: Curvature-Based Multitask Learning Deep Networks for 3D Object Recognition 期刊论文 IEEE/CAA Journal of Automatica Sinica, 2021, 卷号: 8, 期号: 6, 页码: 1177-1187 作者: A. A. M. Muzahid; Wanggen Wan; Ferdous Sohel; Lianyao Wu; Li Hou 收藏 \| 浏览/下载：15/0 \| 提交时间：2021/06/11 3D shape analysis convolutional neural network DNNs object classification volumetric CNN
	L3DOC: Lifelong 3D Object Classification 期刊论文 IEEE TRANSACTIONS ON IMAGE PROCESSING, 2021, 卷号: 30, 页码: 7486-7498 作者: Liu YY(刘宇阳); Cong Y(丛杨); Sun G(孙干); Zhang T(张涛); Dong JH(董家华) 收藏 \| 浏览/下载：15/0 \| 提交时间：2021/09/19 3D object classification lifelong learning point-knowledge task-relevant knowledge distillation
	Multi-View Hierarchical Fusion Network for 3D Object Retrieval and Classification 期刊论文 IEEE Access, 2019, 卷号: Vol.7, 页码: 153021-153030 作者: An-An Liu; Nian Hu; Dan Song; Fu-Bin Guo; He-Yu Zhou 收藏 \| 浏览/下载：10/0 \| 提交时间：2019/11/21 Three-dimensional displays Feature extraction Fuses Visualization Task analysis Solid modeling Aggregates 3D object retrieval 3D object classification 3D shape recognition multi-view
	3D Object Classification Based on Multi Convolutional Neural Networks 会议论文 INTERNATIONAL CONFERENCE ON APPLIED MECHANICS AND MECHANICAL AUTOMATION (AMMA 2017), 2017-01-01 作者: Lu, Mei-qi; Li, Wei; Ning, Ya-guang 收藏 \| 浏览/下载：4/0 \| 提交时间：2019/12/30 3D object classification Convolutional neural network Image classification
	3D Attention-Driven Depth Acquisition for Object Identification 会议论文 ACM SIGGRAPH Asia Conference, 2016 作者: Xu, Kai; Shi, Yifei; Zheng, Lintao; Zhang, Junyu; Liu, Min 收藏 \| 浏览/下载：4/0 \| 提交时间：2019/12/31 3D acquisition depth camera next-best-view object identification attention-based model shape classification
	Research on station mode for attitude determination of multiple targets (EI CONFERENCE) 会议论文 2010 International Conference on Computational and Information Sciences, ICCIS2010, December 17, 2010 - December 19, 2010, Chengdu, Sichuan, China Li M.; Wang J.; Zhao L. 收藏 \| 浏览/下载：27/0 \| 提交时间：2013/03/25 A key problem of attitude determination system is how to locate CMOS cameras and deal with relations among them. In this paper several kinds of multi-CMOS station mode are introduced. Fundamental principles and classification of station mode directly affect on measurement parameters of system. No matter what mode is designed the ultimate goal is to extend Effective Viewing Field (EVF) increase capture ratio of objects and increase measurement precision. Station mode introduced in this paper is made of CMOS cameras because of low cost high speed frame frequency and low power consumption CMOS cameras were putted near from object tested. So sequences of image are legible which are convenient for analysis of three dimensional(3D) attitude. 2010 IEEE.
	路面交通场景中的车辆定位与识别学位论文工学博士, 中国科学院自动化研究所: 中国科学院研究生院, 2009 作者: 张兆翔收藏 \| 浏览/下载：61/0 \| 提交时间：2015/09/02 三维模型物体识别物体分类物体定位物体跟踪拟合度评估演化计算摄像机标定 3D Model Object Recognition Object Classification Object Localization Object Tracking Fitness Evaluation Evolutionary Computing Camera Calibration

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