Stereo matching algorithm based on combined matching cost computation and edge preserving filters

Abstract

The stereo matching <span>process is one of the key areas that impact the stereo vision technologies which are commonly used in the application of three-dimensional reconstructions. The accuracy of the depth information used in three-dimensional reconstruction is directly proportional to the accuracy of the disparity obtained from stereo matching. The challenging issue in the stereo matching process is to determine the accurate corresponding point between the left image and right image, especially for image pairs that have different exposure such as different illumination and image pair with less texture region. In order to increase the accuracy of disparity value, a new stereo matching algorithm is proposed based on the combination of Sum of absolute different and census transform at matching cost computation. guided filter was used in the matching cost aggregation in order to remove noise and preserve the edge of the image. In the optimization step, the winner take all strategy is used to select the minimum matching cost. Finally, a median filter is applied to the initial disparity map for refinement purposes. The experimental results show that the algorithm is effective in reducing the error and improving the accuracy of the disparity map in different illumination regions, less textured regions and different environmental exposure.</span>

Stereo matching algorithm for autonomous vehicle navigation using integrated matching cost and non-local aggregation

“…The combination between CT and Absolute Different resulted a high quality depth map for high definition images [11]. The combination of CT and SAD reported able to improve the accuracy of the disparity map [12]. The combination of SAD and gradient matching proposed by [13] also reported to have a good accuracy of the disparity map.…”

“…After completing the filling process, the weighted median filter from [10] is implemented for final refinement. The final disparity map, 𝑑 𝐹 is refined as (12):…”