Neural architectures for estimating correspondences between images
Ignacio Rocco · HAL (Le Centre pour la Communication Scientifique Directe) · 2020
The goal of this thesis is to develop methods for establishing correspondences between pairs of images in challenging situations, such as extreme illumination changes, scenes with little texture or with repetitive structures, and matching parts of objects which belong to the same class, but which may have large intra-class appearance differences. In summary, our contributions are the following: (i) we develop a trainable approach for parametric image alignment by means of a siamese network model, (ii) we devise a weakly-supervised training approach, which allow training from real image pairs having only annotation at the level of image-pairs, (iii) we propose the Neighbourhood Consensus Networks which can be used to robustly estimate correspondences in tasks where discrete correspondences are required, and (iv) because the dense formulation of the Neighbourhood Consensus Networks is memory and computationally intensive, we develop a more efficient variant that can reduce the memory requirements and run-time by more than ten times.