Analyzing the Domain Shift Immunity of Deep Homography Estimation
This addresses the problem of domain adaptation for researchers and practitioners in computer vision, but it is incremental as it analyzes existing models rather than proposing new ones.
The study investigated the domain shift immunity of deep homography estimation models, finding that they generalize well across datasets without transfer learning due to reliance on local textures like edges and corners.
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.