  Homography estimation is often an indispensable step in many computer vision
tasks. The existing approaches, however, are not robust to illumination and/or
larger viewpoint changes. In this paper, we propose bidirectional implicit
Homography Estimation (biHomE) loss for unsupervised homography estimation.
biHomE minimizes the distance in the feature space between the warped image
from the source viewpoint and the corresponding image from the target
viewpoint. Since we use a fixed pre-trained feature extractor and the only
learnable component of our framework is the homography network, we effectively
decouple the homography estimation from representation learning. We use an
additional photometric distortion step in the synthetic COCO dataset generation
to better represent the illumination variation of the real-world scenarios. We
show that biHomE achieves state-of-the-art performance on synthetic COCO
dataset, which is also comparable or better compared to supervised approaches.
Furthermore, the empirical results demonstrate the robustness of our approach
to illumination variation compared to existing methods.
