Local Disparity Estimation With Three-Moded Cross Census and Advanced Support Weight

The classical local disparity methods use simple and efficient structure to reduce the computation complexity. To increase the accuracy of the disparity map, new local methods utilize additional processing steps such as iteration, segmentation, calibration and propagation, similar to global methods. In this paper, we present an efficient one-pass local method with no iteration. The proposed method is also extended to video disparity estimation by using motion information as well as imposing spatial temporal consistency. In local method, the accuracy of stereo matching depends on precise similarity measure and proper support window. For the accuracy of similarity measure, we propose a novel three-moded cross census transform with a noise buffer, which increases the robustness to image noise in flat areas. The proposed similarity measure can be used in the same form in both stereo images and videos. We further improve the reliability of the aggregation by adopting the advanced support weight and incorporating motion flow to achieve better depth map near moving edges in video scene. The experimental results show that the proposed method is the best performing local method on the Middlebury stereo benchmark test and outperforms the other state-of-the-art methods on video disparity evaluation.