Efficient Edge-Awareness Propagation via Single-Map Filtering

In this paper, we propose an efficient framework for edge-preserving stereo matching. Local methods for stereo matching are more suitable than global methods for real-time applications. Moreover, we can obtain accurate depth maps by using edge-preserving filter for the cost aggregation process in local stereo matching. The computational cost is high, since we must perform the filter for every number of disparity ranges if the order of the edge-preserving filter is constant time. Therefore, we propose an efficient iterative framework which propagates edge-awareness by using single time edgepreserving filtering. In our framework, box filtering is used for the cost aggregation, and then the edge-preserving filtering is once used for refinement of the obtained depth map from the box aggregation. After that, we iteratively estimate a new depth map by local stereo matching which utilizes the previous result of the depth map for feedback of the matching cost. Note that the kernel size of the box filter is varied as coarse-to-fine manner at each iteration. Experimental results show that small and large areas of incorrect regions are gradually corrected. Finally, the accuracy of the depth map estimated by our framework is comparable to the state-of-the-art of stereo matching methods with global optimization methods. Moreover, the computational time of our method is faster than the optimization based method.