Analyzing the Domain Shift Immunity of Deep Homography Estimation

TL;DR

This paper investigates why deep homography estimation models are surprisingly resilient to domain shifts, revealing that their reliance on local textures like edges and corners underpins this immunity, independent of network architecture.

Contribution

The study demonstrates that domain shift immunity in deep homography models is due to their dependence on local textures, not architecture, providing insights into their generalizability.

Findings

Models rely on local textures such as edges and corners.

Immunity to domain shifts is linked to texture reliance, not architecture.

Models maintain performance across different datasets without transfer learning.

Abstract

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.