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Jonas Wulff (Project leader)
Ph.D. Student
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Laura Sevilla
Postdoctoral Researcher
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Michael Black
Director
1 result

2017


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Optical Flow in Mostly Rigid Scenes

Wulff, J., Sevilla-Lara, L., Black, M. J.

In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), July 2017 (inproceedings)

Abstract
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.

pdf SupMat Project Page [BibTex]

2017

pdf SupMat Project Page [BibTex]