Scene Flow Estimation From Sparse Light Fields Using a Local 4D Affine Model
Pierre David, Mikaël Le Pendu, Christine M. Guillemot · IEEE Transactions on Computational Imaging · 2020
In this paper, we address the problem of scene flow estimation from sparsely sampled video light fields. We first propose a local 4D affine model to represent scene flows, taking into account light field epipolar geometry. The model parameters are estimated per cluster in the 4D ray space. They are derived by fitting the model on initial motion and disparity estimates obtained by using 2D dense optical flow estimation techniques. We demonstrate that the model is very effective for estimating scene flows from 2D optical flows. The model regularizes the optical flows and disparity maps, and interpolates disparity variation values in occluded regions. The proposed model allows us to benefit from deep learning-based 2D optical flow estimation methods while ensuring scene flow geometry consistency in the 4 dimensions of the light field.