对于基于块进行立体匹配的深度学习方法而言,网络结构的设计对匹配代价的计算至关重要,同时,卷积神经网络(CNN)在图像处理时的耗时问题也亟待解决。提出一种基于“缩小型”网络的CNN立体匹配方法。利用CNN训练左右图像块的相似性,计算出立体匹配的匹配代价。其中,CNN特征提取阶段,通过对每个层增加相应的批归一化层,可以使训练使用更大的学习率,加快网络训练收敛速度。另外,网络设计中全连接层采用“逐层缩小”的形式,结合上述网络优化和损失函数改善,在保证精度的同时提高了运行速度。使用KITTI数据集对算法进行验证,实验结果证明,相比目前国内外先进方法,本文算法在精度方面有一定优势,相比部分方法,速度有较大提升。

For the stereo matching method of deep learning based on patches, the network structure is vital for the calculation of the matching cost, and the time-consuming of convolutional neural network (CNN) in the image processing field also needs to be solved. We propose a stereo matching method of CNN based on a “shrink network”. The CNN method is utilized to train the similarity of the left and right image patches, and the matching cost of the stereo matching is obtained by the similarity. At the feature extraction stage, by adding batch normalization layers to each layer, the gradient dispersion in the backward propagation can be improved effectively. Besides, the full-connection layer adopts a "layer-by-layer reduction" form with other network optimizations to increase the speed while ensuring the accuracy. We utilize the KITTI datasets to test the algorithm. Experimental results demonstrate that the proposed method increases the accuracy and speed fairly compared to some other methods.