# Semi-Global Stereo Matching Algorithm Based on Multi-Scale Information Fusion
## 1 Introduction  
As a significant research topic in binocular vision, dense stereo matching is widely used in the fields of photogrammetry, 3D reconstruction and automatic driving [1; 2; 3; 4]. Although plenty of high-performance stereo matching algorithms have emerged in recent years, it is still a challenging task to improve the stereo matching accuracy in low-texture regions. According to the difference of constraint range of matching cost function, [PERSON] and [PERSON] [5] divided stereo matching algorithms into local stereo matching and global stereo matching. Local stereo matching algorithms only use the local information around pixels, such as the sum of absolute difference [6] and the sum of squared difference [7], to compute disparity. These local algorithms can be implemented much faster but have low matching accuracy. Global stereo matching algorithms integrate all pixel information in an image by constructing a global matching cost function. A high-precision disparity map can be obtained by minimizing the matching cost function. The commonly used global stereo matching methods include dynamic programming method [8], belief propagation method [9] and graph cut method [10]. These global algorithms have high matching accuracy but low processing efficiency. Overall, it is difficult to balance accuracy and efficiency by using local stereo matching algorithms or global stereo matching algorithms alone. In order to improve the efficiency of global stereo matching algorithms while ensuring matching accuracy, [PERSON] [11] proposed a semi-global stereo matching (SGM) algorithm. In this algorithm, an efficient one-dimensional (1D) path aggregation method was used to replace the two-dimensional (2D) energy minimization method when calculating the energy function minimization. Inference along each scanline was implemented respectively, and then the results in multiple directions were fused to obtain the final cost volume of each pixel. However, due to the high computational complexity in the cost computation stage of the mutual information method, iteration process is required and thus affects the overall efficiency of the algorithm. Meanwhile, two adjacent pixels affect mutually only on the same scanline, it is easy to yield streaking artifacts in disparity image. [PERSON] et al. [12] proposed the more global matching (MGM). By incorporating messages from the nodes visited in the previous scanline, this approach integrated 2D information in the processing of SGM's 1D paths, which effectively reduced the streaking artifacts of SGM. [PERSON] et al. [13] proposed a semi-global and block matching. An adaptive block matching strategy was employed instead of a dense matching strategy to achieve accurate disparity estimation in water areas. [PERSON] et al. [14] introduced a local edge-aware filtering method to SGM to enhance the interaction of neighboring scanlines, which effectively improved the quality of the disparity map. [PERSON] et al. [15] proposed a novel omni-directional SGM (OmniSGM). By constructing a cost volume update scheme, OmniSGM aggregated costs from paths in all directions and encouraged beneficial information to propagate throughout the image, achieving excellent matching results. However, the common shortcoming of these methods is that they only consider the original image in a single scale and ignore the use of information in other scales. Therefore, it is still struggles in dealing with low-texture regions. Inspired by the human visual system that observes from coarse scene to fine scene, [PERSON] et al. [16] introduced the cross-scale concept in the cost aggregation of stereo matching and proposed the cross-scale cost aggregation (CSCA) framework. Under this framework, cost aggregation methods such as bilateral filter (BF) [17], guided filter (GF) [18], non-local cost aggregation (NL) [19] and minimum spanning tree (ST) [20] were used for multi-scale stereo matching.