Anisotropic Median Filtering for Stereo Disparity Map Refinement

Abstract

In this paper we present a novel method for refining stereo disparity maps that is inspired by both simple median filtering and edge-preserving anisotropic filtering. We argue that a combination of these two techniques is particularly effective for reducing the fattening effect that typically occurs for block-matching stereo algorithms. Experiments show that the newly proposed post-refinement can propel simple patch-based algorithms to much higher ranks in the Middlebury stereo benchmark. Furthermore, a comparison to state-of-the-art methods for disparity refinement shows a similar accuracy improvement but at only a fraction of the computational effort. Hence, this approach can be used in systems with restricted computational power.