# Hash Indexing-Based Image Matching for 3D Reconstruction
## 2 Related Works
### Image Matching  
Image matching has obtained great progress in the past decades due to many potential applications, so various feature-matching methods have been proposed by researchers [24; 25]. In a nutshell, existing methods can be roughly divided into two categories according to the types of input data (video, and images). If the input data are in video form, the KLT and variants are usually adopted to locate feature correspondences by computing the displacements between two consecutive frames [26]. The Lucas and Kanade tracker (KLT) [27] is generally considered to be the best choice for video feature tracking, especially in robotic navigation, trajectory extraction for moving objects, and visual object tracking. [PERSON] et al. [28] presented an adaptive KLT that was implemented in parallel programming for real-time applications. [PERSON] et al. [29] proposed the use of the inertial measurement unit (IMU) to improve the stability of KLT for a moving camera. [PERSON] et al. [30] presented a novel framework for jointly tracking a collection of feature points, which can share information between the different feature points in the scene. This method achieves stable feature tracking via subspace constraints. [PERSON] et al. [31] formulated the feature tracking problem as a spatio-temporal statistical learning framework, so they proposed a robust keypoint tracking method via metric learning-driven multi-task structured output optimization. Generally, KLT and its variants can produce promising results for video sequences. Regarding the second category, there have also been many approaches published in recent years. For example, [PERSON] et al. [31] proposed an efficient feature-tracking method to process a nonconsecutive image sequence for large-scale SFM, in which the parallel SIFT feature and second-pass matching strategy were adopted to handle feature-tracking drift. [PERSON] et al. [32] developed a novel method called RepMatch for reconstructing the city model, in which the Epipolar guided approach is adopted to accommodate both wide baselines and repeated structures. Additionally, the RepMatch also utilized random sample consistency (RANSAC) to handle different true or false feature correspondences. [PERSON] et al. [33] proposed wide-baseline image matching via projective view synthesis for two-view image matching. This method can aptly handle the limitations caused by the approximate character of affine transformations via projective transformations, and can also use the essential matrix between images to reject incorrect feature correspondences. Recently, [PERSON] et al. [34] proposed to make use of local geometric priors to boost the spatial verification for filtering outliers. This method also utilized the scale and orientation information from feature neighborhoods to identify which feature points are likely to be correct feature correspondences and has proved to be excellent in practice. Recently, the deep learning technique has provided excellent performances in various computer vision tasks, such as object detection and recognition, visual tracking and localization, and depth estimation. Thus, some researchers have tried to use deep learning techniques to handle image matching, including feature point detection, descriptor computing, and feature matching. For example, the first work of deep learning-based image matching was MatchNet, proposed by [PERSON] et al. [22], in which an end-to-end neural network architecture was designed to generate feature correspondences for image pairs. [PERSON] et al. [35] presented a hierarchical neural network named NM-Net to generate high-precision feature correspondences via mining reliable neighbors, in which NM-Net takes the generated graph as an input, then boosts the robustness to the order of feature correspondences. [PERSON] et al. [36] designed a deep neural network architecture for the purpose of learning to locate good feature correspondences for wide-baseline images.