Image Alignment via Multi-Model Geometric Fitting and Hierarchical Homography Estimation

It is challenging to achieve accurate alignment for building images containing multiple planes. We propose a multi-model geometric fitting and hierarchical homography estimation method to improve the alignment performance for building images. We first extract scale-invariant feature transform (SIFT) features of the images, and then adopt the multi-homography fitting algorithm to classify the feature points into different deformation models. According to the deduced deformation models, we partition the source image into base and transition regions. For the base regions, we adopt the moving direct linear transformation (Moving DLT) to estimate homographies. For the transition regions, we propose a hierarchical homography estimation method to select appropriate homographies. Experimental results show that our method achieves more accurate alignment results compared with state-of-the-art alignment methods for building images.