Self-Supervised deep homography estimation with invertibility constraints

•We propose a self-supervised regression network for homography estimation by generating synthetic images in a cyclic way.•We propose the invertibility loss which contributes to avoid over-fitting and improve the homography estimation accuracy.•Experiments on MS-COCO dataset show that our SSR-Net outperforms state-of-the-art traditional and self-supervised methods.