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Layered image motion with explicit occlusions, temporal consistency, and depth ordering
Deqing Sun · Erik Sudderth · Michael J Black

Mon Dec 06 12:00 AM -- 12:00 AM (PST) @

Layered models are a powerful way of describing natural scenes containing smooth surfaces that may overlap and occlude each other. For image motion estimation, such models have a long history but have not achieved the wide use or accuracy of non-layered methods. We present a new probabilistic model of optical flow in layers that addresses many of the shortcomings of previous approaches. In particular, we define a probabilistic graphical model that explicitly captures: 1) occlusions and disocclusions; 2) depth ordering of the layers; 3) temporal consistency of the layer segmentation. Additionally the optical flow in each layer is modeled by a combination of a parametric model and a smooth deviation based on an MRF with a robust spatial prior; the resulting model allows roughness in layers. Finally, a key contribution is the formulation of the layers using an image-dependent hidden field prior based on recent models for static scene segmentation. The method achieves state-of-the-art results on the Middlebury benchmark and produces meaningful scene segmentations as well as detected occlusion regions.

Author Information

Deqing Sun (Brown University)
Erik Sudderth (University of California, Irvine)
Michael J Black (Max Planck Institute for Intelligent Systems)

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