Hierarchical Joint Bilateral Filtering for Depth Post-Processing

Abstract

Various 3D applications require accurate and smooth depth map, and post-processing is necessary for depth map directly generated by different correspondence algorithms. A hierarchical joint bilateral filtering method is proposed to improve the coarse depth map. By first carrying out depth confidence measuring, pixels are put into different categories according to their matching confidence. Then the initial coarse depth map is down-sampled together with the corresponding confidence map. Depth map is progressively fixed during multistep up sampling. Different from many filtering approaches, confident matches are propagated to unconfident regions by suppressing outliers in a hierarchical structure. Experiment results present that the proposed method can achieve significant improvement of initial depth map with low computational complexity.