Unsupervised Homography Estimation on Multimodal Image Pair via Alternating Optimization

Understand

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.