Existing methods for image alignment struggle in cases involving feature-sparse regions, extreme scale and field-of-view differences, and large deformations, often resulting in suboptimal accuracy. Robustness to these challenges improves through iterative refinement of the transformation field while focusing on critical regions in multi-scale image representations. We thus propose Auto-Regressive Transformation (ART), a novel method that iteratively estimates the coarse-to-fine transformations within an auto-regressive framework. Leveraging hierarchical multi-scale features, our network refines the transformations using randomly sampled points at each scale. By incorporating guidance from the cross-attention layer, the model focuses on critical regions, ensuring accurate alignment even in challenging, feature-limited conditions. Extensive experiments across diverse datasets demonstrate that ART significantly outperforms state-of-the-art methods, establishing it as a powerful new method for precise image alignment with broad applicability.

本研究解决了现有图像对齐方法在特征稀疏区域、极端尺度和视场差异以及大变形情况下表现不佳的问题。我们提出了一种新颖的自回归变换（ART）方法，能够在自回归框架下迭代地估计自粗到细的变换。经过广泛实验验证，ART显著超越了现有最先进的方法，展示了其在复杂条件下的准确对齐能力。

自回归变换用于图像对齐