# Simplified High-Performance Cost Aggregation for Stereo Matching
## 1 Introduction  
Binocular stereo matching aims to restore 3D information based on a pair of rectified 2D images obtained from the same scene. Due to its passive and low-cost sensing characteristics, the acquired depth information may play a vital role in the guidance of uncrewed aerial/ground vehicles, high-end security, surveillance, and various 3D manipulation, inspection, and measurement applications. Traditional stereo matching algorithms can be categorized into global and local algorithms [1], depending on the extent of information used for matching evaluation. Local methods consider only neighboring pixels for each candidate pixel, while the other methods exploit the entire image information. Traditional matching algorithms normally begin with formulating a stereo matching cost that estimates the matching degree between reference patches and target patches. Global stereo matching algorithms require computationally demanding optimization algorithms, such as graph cuts [2] and dynamic programming [3; 4], to find the disparity of each pixel. To facilitate the algorithms in real time, [5] implemented an integrated scheme combining a dynamic programming algorithm and a local algorithm using a graphics processing unit (GPU), while [6] implemented semi-global stereo matching using a field-programmable gate array (FPGA). Unlike global algorithms, the stereo matching cost of local and non-local/semi-global algorithms is an aggregation of primary matching costs. The aggregation is normally conducted as a filtering procedure, and the resultant disparity map is obtained through the winner-take-all (WTA) strategy [1]. Since the computational complexities of localalgorithms are usually smaller than that of other algorithms, they are widely used in practical applications. Among the early efforts of using filters for cost volume aggregation in local algorithms, the matching performance of [7] was constrained by a fixed supporting window. This shortage was alleviated in [8] by introducing an adaptive window-size approach. Later, the Guided Image Filtering (GIF) model [9] was successfully implemented in [10], which demonstrates an edge-preserving advantage. Based on [9; 11] further proposed a weighted guided image filter (WGIF) scheme to avoid halo artifacts and was used in [12] for disparity estimation. As the matching performance of GIF depends on the size of kernel windows, [13] proposed an adaptive guided filtering method to exclude pixels that do not belong to the same region. Besides, an iterative guided filtering approach [14] and an adaptive support weight version [15] were created to improve matching accuracy. More recently, [16] proposed weights according to structural features and filtered the matching cost volume by using the adaptive guided filtering method proposed by [13]. These are typical local stereo matching algorithms that do not aggregate matching costs outside the supporting window. In [17], matching costs were aggregated according to tree structures individually derived from the entire image pair. Similarly, matching algorithms based on the so-called permeability filter [18] and pervasive guided image filtering [19] can effectively aggregate matching costs based on the whole image. In addition [20], integrated multi-scale information into the scheme of [19], significantly improving the matching performance. These algorithms use the full window for aggregation and are called non-local stereo matching algorithms. To solve the problem of matching ambiguity in low-texture areas and high sensitivity in high-texture areas [21], proposed the use of both the local support window and the whole image. In addition to these traditional algorithms, disparity maps can also be computed using deep learning-based methods. These algorithms have the advantage of high matching accuracy. For instance [22], developed an automatic encoder to generate feature maps for semi-global stereo matching, and [23] implemented an unsupervised disparity estimation neural network based on the principle of disparity consistency.