Full View Optical Flow Estimation Leveraged From Light Field Superpixel

Hao Zhu, Xiaoming Sun, Qi Zhang, Qing Wang, Antonio Robles-Kelly, Hongdong Li, Shaodi You

    Research output: Contribution to journalArticlepeer-review


    In this paper, we present a full view optical flow estimation method for plenoptic imaging. Our method employs the structure delivered by the four-dimensional light field over multiple views making use of superpixels. These superpixels are four dimensional in nature and can be used to represent the objects in the scene as a set of slanted-planes in three-dimensional space so as to recover a piecewise rigid depth estimate. Taking advantage of these superpixels and the corresponding slanted planes, we recover the optical flow and depth maps by using a two-step optimization scheme where the flow is propagated from the central view to the other views in the imagery. We illustrate the utility of our method for depth and flow estimation making use of a dataset of synthetically generated image sequences and real-world imagery captured using a Lytro Illum camera. We also compare our results with those yielded by a number of alternatives elsewhere in the literature.
    Original languageEnglish
    Pages (from-to)12-23
    JournalIEEE Transactions on Computational Imaging
    Publication statusPublished - 2020


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