Efficient Dense Scene Flow from Sparse or Dense Stereo Data
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Joint estimation of motion, structure and geometry from stereo sequences
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Stereoscopic Scene Flow Computation for 3D Motion Understanding
International Journal of Computer Vision
Bundle adjustment for stereoscopic 3D
MIRAGE'11 Proceedings of the 5th international conference on Computer vision/computer graphics collaboration techniques
A loop-consistency measure for dense correspondences in multi-view video
Image and Vision Computing
Multi-view Scene Flow Estimation: A View Centered Variational Approach
International Journal of Computer Vision
Consistent Binocular Depth and Scene Flow with Chained Temporal Profiles
International Journal of Computer Vision
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We propose an energy-based joint motion and disparity estimation algorithm with an anisotropic diffusion operator to yield correct and dense displacement vectors. The model estimates the left and right motions simultaneously in order to increase accuracy. We use the Euler-Lagrange equation with variational methods and solve the equation with the finite difference method (FDM). Then, the method computes the initial disparity in the current frame with joint estimation constraint, and regularizes this disparity by using our energy model. Experimental results show that the proposed algorithm provides accurate motion-disparity maps, and preserve the discontinuities of the object boundaries well.