Unsupervised Homography Estimation on Multimodal Image Pair via Alternating Optimization Sanghyeob Song Jaihyun Lew 1

Estimating the homography between two images is crucial for mid-or high-level vision tasks, such as image stitching and fusion. However, using supervised learning methods is often challenging or costly due to the difficulty of collecting ground-truth data. In response, unsupervised learning approaches have emerged. Most early methods, though, assume that the given image pairs are from the same camera or have minor lighting differences. Consequently, while these methods perform effectively under such conditions, they generally fail when input image pairs come from different domains, referred to as multimodal image pairs.