Variational Densification and Refinement of Registration Maps

Abstract

Local patch-based algorithms for image registration fail to accurately match points in areas not discriminative enough, mainly textureless regions. These methods normally involve a validation process and provide a non-completely dense solution. In this paper, we propose a novel refinement and completion approach for registration. The proposed model combines single image nonlocal densification with classical variational image registration. We associate a total variation regularization with a nonlocal term to provide a smooth solution leveraging the image geometry. We show experiments on public stereo and optical flow datasets to filter and densify incomplete depth maps and motion fields. Extensive comparisons against existing and state-of-the-art depth/motion fields densification approaches demonstrate the competitive performance of the introduced method. Additionally, we illustrate how our method can deal with other tasks, such as filtering and interpolation of depth maps from RGBD data and depth upsampling.