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Extending Layered Models to 3D Motion

  • Dong LaoEmail author
  • Ganesh Sundaramoorthi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11214)

Abstract

We consider the problem of inferring a layered representation, its depth ordering and motion segmentation from video in which objects may undergo 3D non-planar motion relative to the camera. We generalize layered inference to that case and corresponding self-occlusion phenomena. We accomplish this by introducing a flattened 3D object representation, which is a compact representation of an object that contains all visible portions of the object seen in the video, including parts of an object that are self-occluded (as well as occluded) in one frame but seen in another. We formulate the inference of such flattened representations and motion segmentation, and derive an optimization scheme. We also introduce a new depth ordering scheme, which is independent of layered inference and addresses the case of self-occlusion. It requires little computation given the flattened representations. Experiments on benchmark datasets show the advantage of our method over existing layered methods, which do not model 3D motion and self-occlusion.

Keywords

Motion Video segmentation Layered models 

Supplementary material

474197_1_En_27_MOESM1_ESM.pdf (15.5 mb)
Supplementary material 1 (pdf 15885 KB)

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.KAUSTThuwalSaudi Arabia

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