Homography Estimation

Homography estimation aims to find the geometric transformation between two images of the same planar scene, crucial for tasks like image stitching, visual localization, and 3D reconstruction. Recent research emphasizes unsupervised and cross-modal approaches, employing deep learning architectures like transformers and convolutional neural networks, often incorporating self-supervised learning and incorporating additional sensor data (e.g., IMU, GPS) to improve robustness and accuracy. These advancements are significantly impacting various fields, including autonomous driving, robotics, and remote sensing, by enabling more accurate and reliable image-based analysis and scene understanding.