目的 立体匹配算法是立体视觉研究的关键点，算法的匹配精度和速度直接影响3维重建的效果。对于传统立体匹配算法来说，弱纹理区域、视差深度不连续区域和被遮挡区域的匹配精度依旧不理想，为此选择具有全局匹配算法和局部匹配算法部分优点、性能介于两种算法之间、且鲁棒性强的半全局立体匹配算法作为研究内容，提出自适应窗口与半全局立体匹配算法相结合的改进方向。方法 以通过AD（absolute difference）算法求匹配代价的半全局立体匹配算法为基础，首先改变算法匹配代价的计算方式，研究窗口大小对算法性能的影响，然后加入自适应窗口算法，研究自适应窗口对算法性能的影响，最后对改进算法进行算法性能评价与比较。结果 实验结果表明，匹配窗口的选择能够影响匹配算法性能、提高算法的适用范围，自适应窗口的加入能够提高算法匹配精度特别是深度不连续区域的匹配精度，并有效降低算法运行时间，对Cones测试图像集，改进的算法较改进前误匹配率在3个测试区域平均减少2.29%；对于所有测试图像集，算法运行时间较加入自适应窗口前平均减少28.5%。结论 加入自适应窗口的半全局立体匹配算法具有更优的算法性能，能够根据应用场景调节算法匹配精度和匹配速度。

Objective Stereovision is the current research focus in the field of computer vision, and its main research content is to reconstruct a 3D scene through two or more 2D images of the same scene. Stereovision has been widely used in the fields of military, aerospace, and unmanned aerial vehicle, which require 3D reconstruction and speed measurement. The stereovision system generally consists of four basic processes:image acquisition, camera calibration, stereo matching, and 3D reconstruction. Research on the stereo matching algorithm can be considered the key point of stereovision research because the matching accuracy and speed of the stereo algorithm directly affect the result of 3D reconstruction. Therefore, research on stereo matching algorithm has great practical value and theoretical significance. However, the traditional stereo matching algorithm suffers from problems of weak matching in regions with weak textures, deep discontinuities, and non-occlusion. Therefore, we select the semi-global stereo matching algorithm, which has strong robustness and some advantages of global and local matching algorithms. Furthermore, we propose an improved method that combines adaptive window and semi-global stereo matching algorithms. Method Our algorithm improvement is based on the adaptive window and semi-global stereo matching algorithms, and it uses the absolute difference (AD) algorithm to calculate the matching cost. First, we changed the original AD algorithm to the sum of absolute differences (SAD) algorithm to obtain the matching cost, which provides the possibility of implementing the adaptive window. Thereafter, we analyzed the necessity and rationality of assuming an adaptive window by studying the effect of window size on the performance of the SAD and semi-global stereo matching algorithms. Furthermore, we added the adaptive window algorithm to the SAD and semi-global stereo matching algorithms to study the effects of the adaptive window on the performance of the SAD and semi-global stereo matching algorithms. In this part, we proposed a new parameter adaptive window judgment threshold. We tested the influences of this judgment threshold on the matching algorithm. Next, we evaluated the performance of the algorithms and compared them in terms of the optimal matching precision and matching speed by using the standard test image pairs provided by the test platform. Finally, we used a binocular camera to obtain left and right views in a real indoor scene. We further compared the performance of the above stereo matching algorithms by using a disparity map and by analyzing the algorithms' runtime. Result The experimental results show that the selection of the size of the matching window can affect the performance of the matching algorithm and improve the applicable range of the algorithm. The addition of the adaptive window could improve the matching accuracy of the algorithm, especially in the depth discontinuous region, and effectively reduce the runtime of the algorithm. After adding the adaptive window algorithm, a large preset maximum window corresponds to more evident optimization of the algorithm runtime. However, the change in matching accuracy is uncertain, which may be improved or decreased. As for the effects of window size judgment threshold, the optimal number of judgment thresholds varies in different standard test image pairs, and the judgment thresholds have different effects on the SAD and the semi-global stereo matching algorithms. The window size judgment threshold has minimal influence on the performance of the semi-global stereo matching algorithm. Thus, the choice of the number of window size judgment threshold is more flexible. The optimal window size of the semi-global stereo matching algorithm is small due to the influence of other parameters (penalty coefficient and threshold) on the performance of the algorithm, and the adaptive window performs limited optimization of the runtime of the algorithm. For the test image pair cones, the improved semi-global stereo matching algorithm mismatch rate is reduced by 2.29% on average in three test areas, and for all test image pairs, the runtime of the algorithm is reduced by 28.5%. Conclusion In this paper, we present an improved algorithm that combines the adaptive window and semi-global stereo matching algorithms. This improved algorithm was evaluated on standard image pairs, and its performance of our algorithm was compared with that of conventional algorithms. The improved algorithm showed competitive processing time results and accuracy in cones and teddy image pairs, which have a rich texture and a large disparity range. Such approach could optimize the matching accuracy and runtime despite being evaluated on image pairs with a weak texture and small disparity range. This paper contains detailed experimental results of the mismatch rate and runtime of different matching algorithms in four standard test image pairs and three image test areas. We conclude that our algorithm has the advantages of improving matching accuracy in depth discontinuity regions, effectively reducing the runtime, and adjusting the matching accuracy and speed according to the application scene.