Improved Cost Computation and Adaptive Shape Guided Filter for Local Stereo Matching of Low Texture Stereo Images

Hua Liu,Yuanping Xia,Rui Wang,Xiaoming Zhang

doi:10.3390/app10051869

Abstract

Dense stereo matching has been widely used in photogrammetry and computer vision applications. Even though it has a long research history, dense stereo matching is still challenging for occluded, textureless and discontinuous regions. This paper proposed an efficient and effective matching cost measurement and an adaptive shape guided filter-based matching cost aggregation method to improve the stereo matching performance for large textureless regions. At first, an efficient matching cost function combining enhanced image gradient-based matching cost and improved census transform-based matching cost is introduced. This proposed matching cost function is robust against radiometric variations and textureless regions. Following this, an adaptive shape cross-based window is constructed for each pixel and a modified guided filter based on this adaptive shape window is implemented for cost aggregation. The final disparity map is obtained after disparity selection and multiple steps disparity refinement. Experiments were conducted on the Middlebury benchmark dataset to evaluate the effectiveness of the proposed cost measurement and cost aggregation strategy. The experimental results demonstrated that the average matching error rate on Middlebury standard image pairs is 9.40%. Compared with the traditional guided filter-based stereo matching method, the proposed method achieved a better matching result in textureless regions.

Highlights

Dense stereo matching is a significant research topic in the field of photogrammetry and computer vision, greatly benefiting applications like 3D reconstruction, DSM (Digital Surface Model) production, visual reality and autonomous vehicles [1,2,3,4]
The percentage of bad pixels of the estimated disparities over the stereo pairs was served as evaluating criterion
In order to obtain better disparity maps in large textureless regions, this paper proposed a local stereo matching method using efficient combined matching cost measurement and adaptive shape guided filter

Summary

Introduction

Dense stereo matching is a significant research topic in the field of photogrammetry and computer vision, greatly benefiting applications like 3D reconstruction, DSM (Digital Surface Model) production, visual reality and autonomous vehicles [1,2,3,4]. According to the classical taxonomy method proposed by Scharstein and Szeliski [5], these existing stereo algorithms can be mainly classified into global and local approaches. Belief propagation [6,7], graph cuts [8], and dynamic programming [9] are among the most commonly used global stereo matching optimization algorithms. They usually produce a more accurate disparity map than local methods but with higher computational complexity. Local stereo matching algorithms only use the local information

Methods

Results

Conclusion