Approximate geodesic distance tree filter for cost aggregation

Abstract

The computation of the disparity for the pixels in the weak texture area has always been a difficult task in stereo vision. The non-local method based on a minimum spanning tree (MST) provides a solution to construct content-adaptive support regions to perform cost aggregation. However, it always introduces error disparity in slanted surfaces and is sensitive to noise and highly textural regions. The window-based methods are not effective for information dissemination. To overcome the problem mentioned above, this paper proposes an approximate geodesic distance tree filter, which utilizes geodesic distance as a pixels similarity metric and recursive techniques to perform the filtering process. The filtering process is performed recursively in four directions (namely from top-left, top-right, and vice versa), which make our filter with linear complexity. Our filter has advantages in the sense that: (1) the pixel similarity metric is approximated geodesic distance; (2) the computational complexity is linear to the image pixel. Due to these reasons, the proposed method can properly cope with cost aggregation in the textureless regions and preserve the boundary of disparity maps. We demonstrate the strength of our filter in several applications.