解决图像尺度差异大时的配准难题，精度在8倍缩放下仍保持领先。
SA-Homo: Scale Adaptive Homography Estimation for Scale Variation Scenarios
- 分层设计：先全局粗对齐，再局部精调，适应不同尺度变化。
- 在8倍尺度差异下仍保持高精度，优于当前最优方法。
- 适合遥感图像配准、跨尺度视觉匹配等场景使用。
单应性估计是计算机视觉中的基础问题，但在图像对存在显著尺度差异的场景中仍具挑战性。现有深度学习方法因依赖有限位移假设和局部特征一致性，在大规模尺度差异下性能明显下降。本文提出SA-Homo，一种新型自适应尺度单应性估计框架，可在广泛尺度差异下实现鲁棒对齐。采用分层尺度对齐策略：先通过包含多尺度线性注意力级联（MLAC）与跨尺度相似性矩阵块（CSMB）的尺度感知差异桥接模块（SDBM）进行全局初始对齐，捕捉长程依赖并缓解特征不一致；随后利用轻量级迭代单应性精修模块（IHERM）基于局部相关性逐步优化结果。为支持研究，我们构建了HMSA数据集——一个高分辨率、多模态卫星基准数据集，专为尺度变异挑战设计。大量实验表明，SA-Homo在8×尺度差异下仍保持高精度，无论在常规相似尺度场景还是复杂尺度变化场景均超越现有先进方法。代码与数据集已开源。
原文摘要 · Abstract (English)
Homography estimation, as one of the fundamental problems in computer vision, remains challenged by scale variation scenarios where image pairs potentially exhibit significant scale discrepancies. Existing deep learning frameworks frequently suffer from a significant performance degradation in such cases, as they rely on limited displacement assumptions and local feature consistency that might not hold under large scale gaps. In this paper, we propose SA-Homo, a novel scale-adaptive homography estimation framework designed to achieve robust alignment across a wide range of scale discrepancy ratios. We adopt a hierarchical scale alignment strategy that transitions from the global perspective with a heavy module to a local perspective with a light module. Specifically, we introduce the Scale-aware Discrepancy Bridging Module (SDBM) for initial alignment, which utilizes a Multi-scale Linear Attention Cascade (MLAC) to capture long-range dependencies and mitigate feature inconsistencies, along with a global Cross-scale Similarity Matrix Block (CSMB) for scale robust correlation representation. Once the initial scale gap is bridged, a lightweight Iterative Homography Estimation Refinement Module (IHERM) progressively polishes the result using local correlations. To facilitate this research, we contribute the HMSA dataset, a high-resolution, multi-modal satellite benchmark specifically tailored for scale-variant challenges. Extensive experiments demonstrate that SA-Homo maintains high precision even under 8$\times$ scale discrepancies, outperforming state-of-the-art methods in both conventional scale-similar scenarios and challenging scale variation scenarios. Code and collected datasets are available at https://github.com/shangxuanx330/SA_Homo
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