通过因果学习与几何拓扑融合，提升街景与航拍图跨视角定位精度。
CVGL: Causal Learning and Geometric Topology
- 用因果干预让模型聚焦稳定语义，减少干扰因素影响。
- 引入鸟瞰路网拓扑，缓解极端视角差异带来的不一致问题。
- 适合关注真实场景下图像定位鲁棒性的研究者和工程师。
跨视角地理定位（CVGL）旨在通过匹配街景图像与对应航拍图像来估计其地理位置，对自动驾驶与地图构建至关重要。然而，由于视角差异显著及混淆因素影响，该任务仍具挑战性。为此，我们提出因果学习与几何拓扑（CLGT）框架，包含两个核心组件：因果特征提取器（CFE）通过因果干预抑制混淆因素影响，引导模型关注稳定、任务相关的语义；几何拓扑融合（GT Fusion）模块将鸟瞰图（BEV）道路拓扑注入街景特征，缓解因极端视角变化导致的跨视图不一致性。此外，我们设计数据自适应池化（DA Pooling）模块以增强语义丰富区域的表征能力。在CVUSA、CVACT及其鲁棒性增强变体（CVUSA-C-ALL和CVACT-C-ALL）上的大量实验表明，CLGT在复杂现实污染条件下均达到领先性能。代码已开源于https://github.com/oyss-szu/CLGT。
原文摘要 · Abstract (English)
Cross-view geo-localization (CVGL) aims to estimate the geographic location of a street image by matching it with a corresponding aerial image. This is critical for autonomous navigation and mapping in complex real-world scenarios. However, the task remains challenging due to significant viewpoint differences and the influence of confounding factors. To tackle these issues, we propose the Causal Learning and Geometric Topology (CLGT) framework, which integrates two key components: a Causal Feature Extractor (CFE) that mitigates the influence of confounding factors by leveraging causal intervention to encourage the model to focus on stable, task-relevant semantics; and a Geometric Topology Fusion (GT Fusion) module that injects Bird's Eye View (BEV) road topology into street features to alleviate cross-view inconsistencies caused by extreme perspective changes. Additionally, we introduce a Data-Adaptive Pooling (DA Pooling) module to enhance the representation of semantically rich regions. Extensive experiments on CVUSA, CVACT, and their robustness-enhanced variants (CVUSA-C-ALL and CVACT-C-ALL) demonstrate that CLGT achieves state-of-the-art performance, particularly under challenging real-world corruptions. Our codes are available at https://github.com/oyss-szu/CLGT.
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