Zhu, HaoSun, XiaomingZhang, QiWang, QingRobles-Kelly, AntonioLi, HongdongYou, Shaodi2023-12-062333-9403http://hdl.handle.net/1885/307699In 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.This work was supported by the NSFC under Grant 61531014. The work of H. Zhu was supported by the CSC Scholarship. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Ivana Tosicapplication/pdfen-AU© 2019 IEEEDepth estimationlight fieldlight field depthsuperpixelscene flow estimation202010.1109/TCI.2019.28979372022-09-04