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Conference papers

Dense Semi-Rigid Scene Flow Estimation from RGBD images

Julian Quiroga 1, 2 Thomas Brox 3 Frédéric Devernay 1, * James L. Crowley 1, *
* Corresponding author
1 PRIMA - Perception, recognition and integration for observation of activity
Inria Grenoble - Rhône-Alpes, UJF - Université Joseph Fourier - Grenoble 1, INPG - Institut National Polytechnique de Grenoble , CNRS - Centre National de la Recherche Scientifique : UMR5217
Abstract : Scene flow is defined as the motion field in 3D space, and can be computed from a single view when using an RGBD sensor. We propose a new scene flow approach that exploits the local and piecewise rigidity of real world scenes. By modeling the motion as a field of twists, our method encourages piecewise smooth solutions of rigid body motions. We give a general formulation to solve for local and global rigid motions by jointly using intensity and depth data. In order to deal efficiently with a moving camera, we model the motion as a rigid component plus a non-rigid residual and propose an alternating solver. The evaluation demonstrates that the proposed method achieves the best results in the most commonly used scene flow benchmark. Through additional experiments we indicate the general applicability of our approach in a variety of different scenarios.
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Submitted on : Wednesday, July 9, 2014 - 9:11:20 PM
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Julian Quiroga, Thomas Brox, Frédéric Devernay, James L. Crowley. Dense Semi-Rigid Scene Flow Estimation from RGBD images. ECCV 2014 - European Conference on Computer Vision, Sep 2014, Zurich, Switzerland. ⟨10.1007/978-3-319-10584-0_37⟩. ⟨hal-01021925⟩



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