# PhotoMatch: An Open-Source Tool for Multi-View and Multi-Modal Feature-Based Image Matching
## 1 Introduction  
Feature-based image matching is a process that provides a correspondence between two or more images connecting basically local image features. The development of automatic and accurate image-matching processes has been a traditional problem in the field of photogrammetry and computer vision [1]. At present, modern camera orientation techniques such as Structure from Motion (SfM) or Visual Simultaneous Localization and Mapping (VSLAM) also rely on the extraction of accurate and reliable homologous points between images. Particularly, these correspondence points between images are normally used within the image orientation and self-calibration process, exploiting globally inherent geometric constraints in an optimization scheme known as bundle adjustment. Image matching can be used for object recognition and tracking, including some specifically hand-crafted features [2, 3] and, more recently, deep learning approaches [4, 5, 6, 7]. The spread of smartphones with powerful cameras, as well as the development of automatic tools for the creation of 3D models from a set of images, has led to the democratization and popularization of photogrammetry and computer vision. At first, photogrammetry was applied only by experts with good knowledge and expertise and using very specialized equipment. At present, techniques such as SfM, together withmulti-view stereo (MVS), allow for the creation of 3D models by end-users without specific knowledge [8; 9; 10; 11; 12]. The creation of 3D models from images acquired by non-experts also presents a challenge for image matching, since the basic rules and protocols for imagery acquisition are often not fulfilled [13]. These amateur users will often acquire images with low overlap, at different scales and perspectives, or even with large differences in lighting or other radiometric conditions. Although modern matching techniques cope with images with radiometric and geometric variations, the image matching process is especially challenging in the case of multi-modal images. Multi-modal image matching is performed between images coming from different sensors or different acquisition techniques and those with significant and nonlinear radiometric distortions. The differences can be due to the use of different sensors (e.g., multispectral, thermal, depth cameras), differences in data types (e.g., drawings vs. photography, vector vs. raster), or different illumination conditions (e.g., day/night images). Multi-modal matching is a critical task for a wide range of applications, such as medicine [14], cultural heritage documentation [15], multitemporal monitoring [16] or person re-identification [17; 18], among others. Image matching algorithms can be classified in two large groups: (i) traditional hand-crafted methods, and (ii) learning-based methods. The latter group utilises artificial intelligence for the development of new detectors and descriptors learned from the data [19]. While the hand-crafted feature-extraction methods are well-established in photogrammetric processes, they are not able to overcome important geometric, radiometric and spectral changes. The number of artificial intelligence algorithms that can be used for image matching is rapidly growing. As a consequence, the selection of a suitable combination of detector, descriptor and matching function for a specific case is a complex task [20]. A detailed study must be conducted for each type of data to select the best algorithm from the increasing number of available options. Additionally, it is important not to overlook the manual configuration of certain input parameters, which can be highly theoretical and difficult for end-users to understand. Configuring each option is a time-consuming process, especially when including deep learning methodologies and training processes. Furthermore, there is a lack of tools that facilitate the processing, comparison, and assessment of the different feature-based image matching methodologies.