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Optical Flow in Mostly Rigid Scenes

2017

Conference Paper

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The optical flow of natural scenes is a combination of the motion of the observer and the independent motion of objects. Existing algorithms typically focus on either recovering motion and structure under the assumption of a purely static world or optical flow for general unconstrained scenes. We combine these approaches in an optical flow algorithm that estimates an explicit segmentation of moving objects from appearance and physical constraints. In static regions we take advantage of strong constraints to jointly estimate the camera motion and the 3D structure of the scene over multiple frames. This allows us to also regularize the structure instead of the motion. Our formulation uses a Plane+Parallax framework, which works even under small baselines, and reduces the motion estimation to a one-dimensional search problem, resulting in more accurate estimation. In moving regions the flow is treated as unconstrained, and computed with an existing optical flow method. The resulting Mostly-Rigid Flow (MR-Flow) method achieves state-of-the-art results on both the MPISintel and KITTI-2015 benchmarks.

Author(s): Jonas Wulff and Laura Sevilla-Lara and Michael J. Black
Book Title: Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017
Pages: 6911--6920
Year: 2017
Month: July
Day: 21-26
Publisher: IEEE

Department(s): Perceiving Systems
Research Project(s): Optical Flow for Mostly Rigid Scenes
Bibtex Type: Conference Paper (inproceedings)
Paper Type: Conference

Event Name: IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Event Place: Honolulu, HI, USA

Address: Piscataway, NJ, USA
ISBN: 978-1-5386-0457-1

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BibTex

@inproceedings{Wulff:CVPR:2017,
  title = {Optical Flow in Mostly Rigid Scenes},
  author = {Wulff, Jonas and Sevilla-Lara, Laura and Black, Michael J.},
  booktitle = {Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017},
  pages = {6911--6920},
  publisher = {IEEE},
  address = {Piscataway, NJ, USA},
  month = jul,
  year = {2017},
  month_numeric = {7}
}