Learning for 360° Vision


Date:  Tuesday, May 23

Start Time: 4:50 pm

End Time: 5:20 pm

As a core building block of virtual reality (VR) and augmented reality (AR) technology, and with the rapid growth of VR and AR, 360° cameras are becoming more available and more popular. People now create, share and watch 360° content in their everyday lives, and the amount of 360° content is increasing rapidly. While 360° cameras offer tremendous new possibilities in various domains, they also introduce new technical challenges. For example, the distortion of 360° content in the planar projection degrades the performance of visual recognition algorithms. In this talk, I will explain how we enable visual recognition on 360° imagery using existing models. Our solution improves 360° video processing and builds a foundation for further exploration of this new media.

Track

Session Speakers

  • Yu-Chuan Su is a Research Scientist at Google. He works on both fundamental and applied research in computer vision, particularly focusing on content generation and intelligent photography. Before joining Google, he received his PhD from the University of Texas at Austin in 2019 and an MS from National Taiwan University in 2014. He and his collaborators’ work in automatic videography was recognized with the ACCV Best Application Paper Award in 2016, and he was the recipient of the Google PhD Fellowship in 2017. His research in computer vision and multimedia has been published at CVPR, ECCV, ACCV, NeurIPS and ACM Multimedia.


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Learning for 360° Vision