# PhotoMatch: An Open-Source Tool for Multi-View and Multi-Modal Feature-Based Image Matching
## 4 Conclusions  
A growing number of detectors, descriptors, and matching algorithms are available to extract and match keypoints between images. The most important distinction can be  
Figure 7: Matchings resulting in the best-performing algorithms for each multi-modal dataset.  
made between hand-crafted and learning-based feature-extraction methods. Some of these algorithms for keypoint extraction and matching are well-known and available in different libraries, such as OpenCV, or integrated into SfM tools. Other algorithms require expertise in dealing with source code and programming, and sometimes the use of external libraries. All of them are too abstract to be understood by end-users, requiring the setup of advanced parameters.  
Despite the large quantity of available options provided in the scientific community, there are no educational and open-source multi-view and multi-modal image-matching tools to date, which allow for a comparative assessment of hand-crafted and learning-based algorithms.  
In real-world problems (e.g., 3D reconstruction, image registration for the analysis of different wavelengths, SLAM or digital correlations between 3D and 2D data for applications such as material deformation analysis), selecting the best-matching algorithm and optimal parameters for a specific application is a time-consuming process requiring very specialised knowledge and is not integrated into the existing tools. This situation can easily lead to the adoption of not-optimal solutions and certainly hampers the adoption of new methodologies.  
PhotoMatch provides a solution to this bottleneck, integrating hand-crafted and learning-based algorithms for comparing and assessing feature-based image matching, with special attention to multi-view and multi-modal imagery. PhotoMatch allows for students, researchers, and other end-users to compare and assess different matching methodologies through an educational and friendly environment, and thus to find the best algorithms for different applications. The different case studies exhibit the capabilities of PhotoMatch and its possibility to offer an accurate and reliable input for image orientation and 3D reconstruction. The results also highlight how different combinations of algorithms and setup parameters can lead to significant changes in the validity of the results.  
Of course, PhotoMatch was conceived to support future developments, so future work will include the addition of new deep learning algorithms [36; 40; 61], as well as new detectors and descriptors [62; 63; 64]. These additions will be added to new release versions or presented as plugins. This will allow PhotoMatch to present a wider array of algorithm combinations for the assessment of the different approaches, while maintaining its educational goal and ease of use.  
Conceptualization, D.G.-A., F.R., P.R.-G. and D.H.-L.; methodology, E.R.d.O. and I.B.-G.; software, E.R.d.O.; validation, I.B.-G.; investigation, D.G.-A., F.R., P.R.-G. and D.H.-L.; writing--original draft preparation, I.B.-G.; writing--review and editing, E.R.d.O., I.B.-G., D.G.-A., F.R. and P.R.-G.; supervision, D.G.-A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The PhotoMatch Installer and the source code are available on [[https://github.com/TIDOP-USAL/PhotoMatch/releases](https://github.com/TIDOP-USAL/PhotoMatch/releases)]([https://github.com/TIDOP-USAL/PhotoMatch/releases](https://github.com/TIDOP-USAL/PhotoMatch/releases)).  
The authors declare no conflict of interest.