## 2 Related Works
### Pose Estimation  
Pose estimation is used to estimate the camera's own position and orientation by registering the image acquired by the camera device with the virtual model data. Application scenarios can be divided into indoor and outdoor scenarios. [PERSON] et al. [7] presented an indoor 2D-3D registration method based on scene recognition, which detects and recognizes the target scene in a video frame image to track targets and estimate the camera pose. [PERSON] et al. [8] developed a novel camera localization workflow based on a highly accurate 3D prior map optimized by the RGB-D SLAM method. Outdoor pose estimation is more difficult than indoor pose estimation. The indoor environment is small, so binocular cameras and RGB-D can be used because the distance is relatively close. However, the outdoor area is larger, and the image appearance is highly affected by the environment. [PERSON] et al. [9] presented a 3D registration method for mobile augmented reality of a building environment based on SURFREAK and KLT. [PERSON] et al. [10] presented an image matching method for distorted buildings with automatic viewpoint correction and fusion. [PERSON] et al. [11] proposed an outdoor AR system registration mechanism based on 3D GIS. Most of the existing methods make use of natural outdoor features, but they do not adequately mine the structural features of cities. Additionally, in practice, we find that because street view images often contain a sky background and have a large depth range, they have an uneven distribution of features in the vertical direction, resulting in errors in camera pose estimation in the vertical direction, which affects the camera pose accuracy.  
From the view of technology, the traditional framework of the pose estimation system has three steps: feature extraction, feature matching, and pose calculation. The focus of research is feature extraction and matching. Presently, difficulties are encountered in practical application. First, the appearance of images often differs significantly with a change in season and weather. The key to solving this problem is having a local feature extraction method with high discrimination, such as SIFT [12]. To address the aforementioned shortcomings of traditional feature extraction methods, scholars have applied the convolution neural network (CNN) to image feature extraction, gradually evolved from using handcrafted and deep convolutional features, and successively proposed several methods, including SuperPoint [13] and D2-Net [14]. Deep convolutional features use high-level semantic information for feature point extraction, have strong generalizability, and ultimately show great potential in solving image matching in changing scenes. Second, repeated texture leads to difficulty in matching. There are many repeated texture features, especially in building targets, which place feature point matching requirements on the algorithm. The random sample consensus (RANSAC) algorithm is a widely used matching method, and, while its basic idea is simple and effective, there are two prerequisites for using it [15]: (1) The proportion of interior points must be at a high level because, if less than 50%, the RANSAC method requires a large number of iterations [16] and may fail or become very time-consuming [17]. (2) An assumed model must be given and then satisfied by the interior points [18]. In [19], the spatial constraints of the Delaunay triangulation were used to improve the robustness of feature point matching, but the method was time-consuming. [PERSON] et al. [20] proposed a feature point extraction algorithm that could run in real time; however, its accuracy failed to meet localization requirements. Achieving good performance in challenging environments is difficult when using the existing mainstream feature point matching methods.