# Multi-Scale Aggregation Stereo Matching Network Based on Dense Grouping Atrous Convolution
## 1 Introduction  
Binocular stereo vision is an important branch of computer vision. It processes the real world by simulating the human vision system. In the 1980s, [PERSON] [1] proposed a visual computing theory based on binocular matching, which laid the theoretical foundation for the development of binocular stereo vision. Because binocular vision can obtain rich target information and environment information, it is low cost and has the ability for stereo perception, and it is widely used in robot navigation [2], three-dimensional measurement [3], virtual reality, autonomous driving, and other fields. Stereo matching is an essential part of binocular stereo vision and is mainly divided into traditional and deep learning methods. The traditional binocular stereo matching method relies on the experience of designers to select appropriate features, which has the defect of a low matching accuracy. With the development of CNN [4], the architecture has become an effective way to solve the problems in the binocular stereo matching algorithm. The end-to-end binocular stereo matching in the deep learning method uses CNN throughout the process; this seamlessly integrates all the steps in stereo matching,inputting stereo images and directly outputting dense disparity maps [5]. Compared with the traditional method, it can complete a high-precision depth estimation from stereo image pairs. Therefore, the deep learning method is widely used. End-to-end stereo matching has greatly improved the accuracy of binocular stereo matching, and this precise perception ability is urgently needed for autonomous driving. However, there are still challenges: one such challenge is improving the accuracy of stereo matching, including in the case of moving objects of different sizes. Therefore, it is urgent to design a more effective stereo matching network. The ASPP method [6] proposes using concatenate atrous convolution [7] layers with different dilation rates to obtain receptive fields of different sizes, which is conducive to multi-scale information [8; 9]. The input image in autonomous driving scenes has a high resolution, which requires the neuron to have a larger receptive field. In order to obtain a sufficiently large receptive field in the ASPP method, a sufficiently large dilation rate must be used; however, as the dilation rate increases, the network gradually loses its modeling ability. Taking this into account, the DenseASPP [10] and GASPP [11] methods combine the advantages of the parallel and cascade use of void convolutional layers to obtain larger receptive fields and denser features, and thus improve the recognition ability of the algorithm when the target proportion changes. Although the above methods increase the receptive field to a certain extent, there is not enough attention paid to the contextual information. Another difficulty is the matching accuracy present in challenging low-texture areas. The accuracy of stereo matching is affected by noise between pixels. In the traditional stereo matching method, the SGM [12] approach solves the influence of noise by finding the minimized matching cost energy function, but its energy function and solving process are manually designed, which limits the performance and generalization ability of the algorithm. The 3D CNN approach is able to directly extract features from the original input while simultaneously providing powerful regularization, which can be used in the cost aggregation step of stereo matching to eliminate noise between pixels. GC-Net [13] and PSM-Net [14] approaches both use stacked 3D CNNs for the regularization operations. However, when the image is in the ill-posed region, the traditional 3D convolution aggregation method cannot form a correct understanding of the details of the image, which affects the accuracy of the environment perception. To solve the above issues, we propose a stereo matching network that is different from the previous methods.