Robust Anisotropic Disparity Estimation with Perceptual Maximum Variation Modeling

We present a robust anisotropic dense disparity estimation algorithm which employs perceptual maximum variation modeling. Edge-preserving dense disparity vectors are estimated using a coarse-to-fine diffusive method on iteratively filtered images, i.e. the scale-space. While an energy-minimization framework adjusts local disparity, the edges are efficiently preserved by anisotropic disparity-field diffusion. However, the localization at weak image edges which have small brightness variations is fundamentally difficult. In this paper, perceptual maximum variation modeling prevents the delocalization flow over edges, e.g. over-diffusion and back-diffusion, computed by evaluating small variations. We perform disparity-field diffusion on a perceptually optimized color space, which combines the small differences in both brightness and chromaticity. Additionally a consistency constraint is employed in the modeling to avoid the influence of global color distributions and to enhance important edges as the human vision system does. The experimental results show the excellent localization performance preserving the disparity discontinuity of each object.