Joining three-dimensional and two-dimensional worlds via multi-space registration

In recent years, applications employing 3D and 2D data have emerged, and they all requirematching 3D models to 2D images. Large-scale location recognition systems allow to esti-mate the location where a picture was taken. Geo-localization systems can be useful in theautonomous driving context by performing place recognition in situations where the GPS mightfail. Furthermore, forensic police could use this systems to solve crimes or prevent attacks.Thegoal of this thesis is to find a novel method to register 2D images to 3D point clouds. Deeplearning techniques are employed for such purpose.A stat-of-the-art study of 2D-3D registration and matching methods is carried out. Some tech-niques are implemented to serve as baseline to the novel work developed: a neural networkbased on graphs trained in a triplet-like fashion with a VGG16 architecture to generate cross-domain descriptors of images and point clouds. The presented architecture can achieve similar(and sometimes better) performance than state-of-the-art techniques in the 2D-3D matchingtask.