# Line Matching Based on Viewpoint-Invariance for Stereo Wide-Baseline Aerial Images
## 1 Introduction  
Oblique photogrammetry is a newly developed technology that can effectively create a three-dimensional (3D) model of a city's buildings. Compared with conventional aerial images, oblique images have their own processing challenges, especially feature matching, due to their wide baselines and large viewing angles. Many scholars have conducted research on point matching for oblique images and achieved reasonable results. For example, affine scale-invariant feature transform (ASIFT) simulates a series of changes in viewing angle and obtains more matched points in the case of a wide baseline [1]; [PERSON] et al. used a positioning and orientation system (POS) to obtain attitude anglesin order to establish affine invariance and to achieve the matching of oblique images [2]; the method proposed by [PERSON] et al. is based on matching local affine invariant regions, which provides good results for matching under wide-baseline conditions [3]. However, the characteristics of the line and point are quite different, and processing of the line feature is more complex than the point. Therefore, there is some limitation for the application of line matching. In particular, the length of a line segment is relatively long, so the image area involved is relatively large and wide. In this case, the deformation effect caused by the difference in viewing angles of wide-baseline images is relatively more obvious, making it more difficult to match the line segments. Line matching also provides the basis for achieving 3D structural line reconstruction [4; 5]. Since the application of line matching is of great significance [6; 7; 8; 9; 10; 11], it is very important to solve the problem of line matching for wide-baseline images. From the similarity matching perspective, the existing line matching methods are mainly divided into two categories: those based on the line segment neighborhood's photometric information (such as intensity, gradient, color, etc. ), and those based on geometric constraints of image features. From the number of line segments used for each matching, it can be further divided into two categories: single line-by-line matching and multiple line-group matching. [PERSON] et al. [12] and [PERSON] et al. [13] in their experiments, created descriptors for each line segment to respectively match the texture information from the gray value distribution. [PERSON] et al. [14], [PERSON] et al. [15], and [PERSON] et al. [16] separately established geometric constraints for each line segment and the matched feature points in its neighborhood to achieve a good line matching effect. In addition, [PERSON] et al. combined the texture representation with geometric constraints for line matching, but their method is prone to failure under low-texture or similar textures [17]. A method based on trifocal tensor is used by [PERSON] et al. to match lines from three or more images; however, their method demands more constraints, therefore many false matches can be eliminated, which also leads to a high level of complexity of the algorithm [18]. There are also some studies online matching specifically for wide-baseline images; amongst them, [PERSON] et al. used line segment groups to create local descriptors that are robust to affine distortions and non-coplanar surfaces [19]. However, this method is time-consuming, and may fail when it cannot find enough line segments or when textures are not abundant. In contrast, [PERSON] and [PERSON] worked on point-like local affine invariant matching [20], which has a good matching effect for large viewing angles, but which is not good at dealing with non-coplanar and complex 3D-structured scenes. Moreover, the two adjacent line segment methods proposed by [PERSON] et al. construct ray-point-ray [21] and line-juncture-line [22] structure descriptors. In this method, structured line segments in the first image are used to match with other structured line segments in the second image, so the robustness is relatively strong, and good matching results are achieved.