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Communication Dans Un Congrès Année : 2019

Sparse to Dense Scene Flow Estimation from Light Fields

Résumé

The paper addresses the problem of scene flow estimation from sparsely sampled video light fields. The scene flow estimation method is based on an affine model in the 4D ray space that allows us to estimate a dense flow from sparse estimates in 4D clusters. A dataset of synthetic video light fields created for assessing scene flow estimation techniques is also described. Experiments show that the proposed method gives error rates on the optical flow components that are comparable to those obtained with state of the art optical flow estimation methods, while computing a more accurate disparity variation when compared with prior scene flow estimation techniques.
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Dates et versions

hal-02123544 , version 1 (08-05-2019)

Identifiants

Citer

Pierre David, Mikaël Le Pendu, Christine Guillemot. Sparse to Dense Scene Flow Estimation from Light Fields. ICIP 2019 - IEEE International Conference on Image Processing, Sep 2019, Taïpei, Taiwan. pp.1-5, ⟨10.1109/ICIP.2019.8803520⟩. ⟨hal-02123544⟩
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