视差不连续区域和重复纹理区域的误匹配率高一直是影响双目立体匹配测量精度的主要问题，为此，本文提出一种基于多特征融合的立体匹配算法。首先，在代价计算阶段，通过高斯加权法赋予邻域像素点的权值，从而优化绝对差之和（Sum of Absolute Differences，SAD）算法的计算精度。接着，基于Census变换改进二进制链码方式，将邻域内像素的平均灰度值与梯度图像的灰度均值相融合，进而建立左右图像对应点的判断依据并优化其编码长度。然后，构建基于十字交叉法与改进的引导滤波器相融合的聚合方法，从而实现视差值再分配，以降低误匹配率。最后，通过赢家通吃（Winner Take All，WTA）算法获取初始视差，并采用左右一致性检测方法及亚像素法提高匹配精度，从而获取最终的视差结果。实验结果表明，在Middlebury数据集的测试中，所提SAD-Census算法的平均非遮挡区域和全部区域的误匹配率为分别为2.67%和5.69%，测量200~900 mm距离的平均误差小于2%；而实际三维测量的最大误差为1.5%。实验结果检验了所提算法的有效性和可靠性。

为解决基于深度学习的立体匹配方法面临着网络规模大、网络结构复杂等问题，提出了一个网络规模较小、精度较高的网络结构。该网络在特征提取模块删减修改了复杂冗余的残差层并引入了空洞卷积金字塔池化模块来扩大视野范围，提取更多有用的上下文信息；在代价计算模块中使用了三维卷积层以成本聚合提升立体匹配的精度；最后，在代价聚合模块引用了双边格网模块以较低分辨率的成本量来获取精度较高的视差图。将该网络在KITTI 2015数据集和Scene Flow数据集等主流数据集上进行实验，结果显示，相较于其他主流优秀网络类如金字塔立体匹配网络（Pyramid Stereo Matching Network ，PSM-Net），网络规模参数量减少了约38%，并取得了较高的实验精度，其中Scene Flow数据集的终点误差（End-point Error，EPE）为0.86，是一个同时兼顾速度与精度的立体匹配网络。

Overview: In the stereo matching process, unique pixel features are extracted by aggregating local and global context information. The pixel features on the pole lines of the left and right images are then matched. With the rapid application of deep learning (DL) methods in the field of image processing, end-to-end neural networks of DL are used to estimate the disparity maps. Although CNN-based algorithms have excellent feature representation capabilities, they often exhibit limitations in modeling explicit long-range relationships due to the inherent locality of the convolution operations. For objects with weak textures and large differences in shape and size, the results of using CNN alone are often unsatisfactory. To solve this problem, an improved model STransMNet stereo matching network based on the Swin Transformer is proposed in this paper. We analyze the necessity of the aggregated local and global context information. Then the difference in matching features during the stereo matching process is discussed. The feature extraction module is improved by replacing the CNN-based algorithm with the Transformer-based Swin Transformer algorithm. The rectified left and right images are fed into Swin Transformer module to generate multi-scale features. Then the multi-scale features are fed into the patch expanding module, the transformation of the linear layer, to make them the same size. Finally, the multi-scale features are fused in the channel dimension. The additional multi-scale fusion module makes the features output by the improved Swin Transformer fuse shallow and deep semantic information. The Swin Transformer used to extract the left and right image features is partially shared by the weights. Although weight sharing makes the model converge faster, our proposed feature differentiation loss can only supervise left or right images. If the full weights are shared, it is equivalent to supervising the left and right images at the same time. Partial weight sharing speeds up the convergence of the model to a certain extent. In addition, partial weight sharing enables the model to extract not only the commonalities of left and right image but also the differences. Furthermore, a feature differentiation loss is proposed in this work to improve the model's ability to pay attention to details. The loss is trained by forcing the classification of pixel features on the epipolar line of the left image, which makes each pixel feature unique. The experimental results on the Sceneflow and KITTI datasets show that our algorithm reduces the 3 px error and EPE compared to the previous algorithms. Experiments show that the proposed STransMNet model reduces the matching error and improves the quality of the disparity maps. It shows that the excellent performance of the improved Swin Transformer in capturing long-distance context information is beneficial to improving the accuracy of stereo matching; feature differentiation loss helps to enhance the detailed information of the disparity maps.