Relative depth estimation from dense matches using Graph Neural Networks
This thesis explores the application of Graph Neural Networks (GNNs) for relative depth estimation from dense matches between images. The main goals were to investigate the feasibility of using dense matches alone for this task, assess the performance improvement offered by GNNs over classical methods, and identify the most suitable features. The methodology involved utilizing the matches from Den
