基于像素注意力的双通道立体匹配网络

Pixel attention based siamese convolution neural network for stereo matching

针对现有立体匹配算法在弱纹理、重复纹理、反射表面等病态区域误匹配率高的问题,提出一种基于像素注意力的双通道立体匹配卷积神经网络PASNet,该网络包括双通道注意力沙漏型子网络和注意力U型子网络。首先,通过双通道注意力沙漏型子网络提取输入图像的特征图;其次,通过关联层得到特征图的代价矩阵;最后,利用注意力U型子网络对代价矩阵进行代价聚合,输出视差图。在KITTI数据集上的实验结果表明,所提出的网络能有效解决病态区域误匹配率高等问题,提升立体匹配精度。

Aiming at the problem that the existing stereo matching algorithm has high mismatch rate in ill-posed regions such as weak texture,repeated texture and reflective surface,a new pixel attention siamese neural network is proposed.Our method consists of siamese attention hourglass subnetwork and attention U-shaped subnetwork.Firstly,the feature map of the input image is extracted by the siamese attention hourglass subnetwork.Secondly,the cost matrix of the feature graph is obtained through the correlation layer.Finally,the cost matrix is aggregated by the attention U-shaped subnetwork,and the disparity map is output.Experiments on the KITTI dataset demonstrate that the proposed algorithm can effectively solve the ill-posed problem and improve the stereo matching accuracy.