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Intrinsic Video

2014

Conference Paper

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Intrinsic images such as albedo and shading are valuable for later stages of visual processing. Previous methods for extracting albedo and shading use either single images or images together with depth data. Instead, we define intrinsic video estimation as the problem of extracting temporally coherent albedo and shading from video alone. Our approach exploits the assumption that albedo is constant over time while shading changes slowly. Optical flow aids in the accurate estimation of intrinsic video by providing temporal continuity as well as putative surface boundaries. Additionally, we find that the estimated albedo sequence can be used to improve optical flow accuracy in sequences with changing illumination. The approach makes only weak assumptions about the scene and we show that it substantially outperforms existing single-frame intrinsic image methods. We evaluate this quantitatively on synthetic sequences as well on challenging natural sequences with complex geometry, motion, and illumination.

Author(s): Naejin Kong and Peter V. Gehler and Michael J. Black
Book Title: Computer Vision – ECCV 2014
Volume: 8690
Pages: 360--375
Year: 2014
Month: September

Series: Lecture Notes in Computer Science
Editors: D. Fleet and T. Pajdla and B. Schiele and T. Tuytelaars
Publisher: Springer International Publishing

Department(s): Perceiving Systems
Research Project(s): Intrinsic Video
Bibtex Type: Conference Paper (inproceedings)
Paper Type: Conference

DOI: 10.1007/978-3-319-10605-2_24
Event Name: 13th European Conference on Computer Vision
Event Place: Zürich, Switzerland

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BibTex

@inproceedings{Kong:ECCV:2014,
  title = {Intrinsic Video},
  author = {Kong, Naejin and Gehler, Peter V. and Black, Michael J.},
  booktitle = {Computer Vision -- ECCV 2014},
  volume = {8690},
  pages = {360--375},
  series = {Lecture Notes in Computer Science},
  editors = {D. Fleet  and T. Pajdla and B. Schiele  and T. Tuytelaars },
  publisher = {Springer International Publishing},
  month = sep,
  year = {2014},
  month_numeric = {9}
}