  Homography estimation serves as a fundamental technique for image alignment
in a wide array of applications. The advent of convolutional neural networks
has introduced learning-based methodologies that have exhibited remarkable
efficacy in this realm. Yet, the generalizability of these approaches across
distinct domains remains underexplored. Unlike other conventional tasks,
CNN-driven homography estimation models show a distinctive immunity to domain
shifts, enabling seamless deployment from one dataset to another without the
necessity of transfer learning. This study explores the resilience of a variety
of deep homography estimation models to domain shifts, revealing that the
network architecture itself is not a contributing factor to this remarkable
adaptability. By closely examining the models' focal regions and subjecting
input images to a variety of modifications, we confirm that the models heavily
rely on local textures such as edges and corner points for homography
estimation. Moreover, our analysis underscores that the domain shift immunity
itself is intricately tied to the utilization of these local textures.
