GAMES Webinar 2019 – 124期(三维视觉前沿专题报告)|戴玉超(Northwestern Polytechnical University),高盛华 (上海科技大学)

【GAMES Webinar 2019-124期】(三维视觉前沿专题报告)

报告嘉宾1:戴玉超,Northwestern Polytechnical University

报告时间:2019年12月26日 晚上8:00-8:45(北京时间)

报告题目:Multi-view Geometry Computation: Optimization based Approaches Meet Deep Learning


Computer vision is dedicated in to enabling machines/robots the visual perception ability as human. Geometric computer vision aims at reconstructing and understanding the three-dimensional geometric structure of the observed scene from images and videos, which has important applications in unmanned systems, autonomous driving, robotics, virtual reality/augmented reality and scene analysis. Deep learning, especially deep convolutional neural networks, has great advantages in feature learning and semantic information extraction. How to effectively combine this data-driven model with multi-view geometric model has become an active research area in computer vision. In this talk, I will present a series of recent work from our group in this direction, including how to achieve monocular depth estimation, binocular depth estimation and multi-view stereo under the framework of supervised learning, and how to construct unsupervised learning frameworks for the tasks of self-adaptive stereo, multi-view stereo, optical flow estimation and Stereo-Lidar fusion. The talk will finish with discussions on future research directions.


Yuchao Dai is currently a Professor with School of Electronics and Information at the Northwestern Polytechnical University (NPU). He received the B.E. degree, M.E degree and Ph.D. degree all in signal and information processing from NPU, Xian, China, in 2005, 2008 and 2012, respectively. He was an ARC DECRA Fellow with the Research School of Engineering at the Australian National University, Canberra, Australia from 2014 to 2017 and a Research Fellow with the Research School of Computer Science at the Australian National University, Canberra, Australia from 2012 to 2014. His research interests include structure from motion, multi-view geometry, low-level computer vision, deep learning, compressive sensing and optimization. He won the Best Paper Award in IEEE CVPR 2012, the DSTO Best Fundamental Contribution to Image Processing Paper Prize at DICTA 2014, the Best Algorithm Prize in NRSFM Challenge at CVPR 2017, the Best Student Paper Prize at DICTA 2017 and the Best Deep/Machine Learning Paper Prize at APSIPA ASC 2017. He served as Area Chair for WACV 2019/2020, ICME 2020 and ACM MM 2019.



报告时间:2019年12月26日 晚上8:45-9:30(北京时间)




高盛华,上海科技大学研究员,副教授(终身教授),博导,入选2015年国家青年千人计划。2012博士毕业于新加坡南洋理工大学。2014年加入上海科技大学信息学院。迄今为止,在计算机视觉领域顶级会议和期刊发表50余篇。担任CVPR ICCV等多个Workshop的主席,ICCV’2019,AAAI2019,IJCAI2020领域主席,计算机视觉领域期刊IEEE TCSVT和Neurocomputing的副主编等。他的工作入围 IJCAI2017的最佳学生论文提名。




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