›› 2017, Vol. 32 ›› Issue (3): 430-442.doi: 10.1007/s11390-017-1734-y

Special Issue: Artificial Intelligence and Pattern Recognition; Computer Graphics and Multimedia

• Special Section of CVM 2017 • Previous Articles     Next Articles

Static Scene Illumination Estimation from Video with Applications

Bin Liu1, Kun Xu1,*, Member, CCF, ACM, IEEE, Ralph R. Martin2, Member, ACM, IEEE   

  1. 1. Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China;
    2. School of Computer Science and Informatics, Cardiff University, Cardiff CF24 3AA, U.K
  • Received:2016-12-18 Revised:2017-04-06 Online:2017-05-05 Published:2017-05-05
  • Contact: Kun Xu E-mail:xukun@tsinghua.edu.cn
  • About author:Bin Liu is a Ph.D.student in the Department of Computer Science and Technology,Tsinghua University, Beijing.He received his Bachelor's degree in computer science from the same university in 2013.His research interests include image and video editing.
  • Supported by:

    This work was supported by the National Natural Science Foundation of China (NSFC) and the Israel Science Foundation (ISF),Joint NSFC-ISF Research Program under Grant No.61561146393,the National Natural Science Foundation of China under Grant No.61521002,a research grant from the Beijing Higher Institution Engineering Research Center,and the Tsinghua-Tencent Joint Laboratory for Internet Innovation Technology.

We present a system that automatically recovers scene geometry and illumination from a video, providing a basis for various applications. Previous image based illumination estimation methods either require user interaction or external information in the form of a database. We adopt structure-from-motion and multi-view stereo for initial scene reconstruction, and then estimate an environment map represented by spherical harmonics (as these perform better than other bases). We also demonstrate several video editing applications that exploit the recovered geometry and illumination, including object insertion (e.g. for augmented reality), shadow detection, and video relighting.

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