Attention-Based Grouping Distance Stereo Network

Stereo matching aims to accurately identify corresponding pixels and calculate the positional deviation between two corresponding pixels, which is of great significance to autonomous driving, 3D reconstruction, and other related tasks. Previous works rely on CNN-based feature extractors, lacking the means to utilize non-local context information and being easily affected by ill-posed regions. Additionally, the cost volumes of full correlation and concatenation lead to less necessary information and more parameters, respectively. In this paper, we propose the Attention-based Grouping Distance Stereo Network (AGDNet), which consists of three main modules: a Spatial Criss-Cross Attention module, Multi-head Channel Attention module, and grouping distance volume. The Spatial Criss-Cross Attention module constructs the long-distance-dependent relationship between pixels, which is propitious to making full use of non-local context information. The Multi-head Channel Attention module introduces multi-head attention strategy into the channel attention module, so as to obtain feature maps with a stronger capacity of representation. The grouping distance cost volume provides an efficient representation for measuring the feature similarity. It not only retains the available matching cues in the feature vectors, but also reduces the size of cost volume. Experimental results that evaluated the Scene Flow dataset, KITTI 2015 dataset, and KITTI 2012 dataset demonstrate that our method achieves competitive performance.