For stereo matching based on patch comparing using convolutional neural networks (CNNs), the matching cost estimation is highly dependent on the network structure, and the patch comparing is time consuming for traditional CNNs. Accordingly, we propose a stereo matching method based on a novel shrinking residual CNN, which consists of convolutional layers and skip-connection layers, and the size of the fully connected layers decreases progressively. Firstly, a layer-by-layer shrinking size model is adopted for the full-connection layers to greatly increase the running speed. Secondly, the convolutional layer and the residual structure are fused to improve patch comparing. Finally, the Loss function is re-designed to give higher weights to hard-classified examples compared with the standard cross entropy loss. Experimental results on KITTI2012 and KITTI2015 demonstrate that the proposed method can improve the operation speed while maintaining high accuracy.

对于基于使用卷积神经网络（CNN）进行补丁比较的立体声匹配，匹配成本估算高度依赖于网络结构，而补丁比较对于传统的CNN而言非常耗时。因此，我们提出了一种基于新颖的残差CNN的立体匹配方法，该方法由卷积层和跳跃连接层组成，全连接层的大小逐渐减小。首先，全连接层采用逐层收缩尺寸模型，大大提高了运行速度。其次，将卷积层和残差结构融合在一起，以改善斑块比较。最后，与标准的交叉熵损失相比，损失函数经过了重新设计，可以为硬分类的示例赋予更高的权重。