Computer Vision: A Journey of Pursuing 3D World Understanding

Abstract

The real world we are living in is composed of 3D objects. When a camera takes a picture or video, many of the 3D information inevitably get lost due to the camera projection. As one of the most active fields in AI, computer vision aims to develop algorithms that can derive meaningful information from the visual content. One fundamental quest of computer vision is to recover the 3D information, and thus enables a faithful 3D understanding of the world through the lens of the camera. In this talk, I will share some of our experiences in pursuing the 3D world understanding, addressing problems such as 3D reconstruction, 3D detection, depth estimation, velocity estimation, etc. The solutions to these problems have been applied to applications including biometrics, autonomous driving, and digital human/face. In the end, I will also briefly overview other research efforts in the Computer Vision Lab at Michigan State University, such as anti-spoofing, anti-deepfake, fair face recognition, etc.

Speaker/s

csrankings.org. He received the 2018 Withrow Distinguished Scholar Award from MSU. He has been Area Chairs for numerous conferences, the Co-Program Chair of BTAS’18, WACV’18, IJCB’22 and AVSS’22 conferences, and Co-General Chair of FG’23 conference. He is an Associate Editor of Pattern Recognition and IEEE Transaction on Image Processing. He has authored more than 160 scientific publications, and has filed 29 U.S. patents. His work has been cited more than 19,000 times according to Google Scholar, with an H-index of 66. He is a fellow of International Association for Pattern Recognition (IAPR). His research has been widely reported in prominent national and international news outlets including the Wall Street Journal, CNBC, CNET, Engadget, Fortune, the Mac Observer, MSU Today, New Scientist, Silicon Angle, VentureBeat, and the Verge.  More information of  Liu’s research can be found at http://cvlab.cse.msu.edu

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