VGA：以视觉几何对齐赋能空地定位
VGA: Empowering Aerial-Ground Localization by Visual Geometry Alignment
把单目透视场推断出的重力对齐先验与 BEV 平面上的 Procrustes 对齐结合，可在未标定、超大基线与小重叠条件下估计 6DoF 相对相机姿态，为跨视角定位与标定提供可复用的几何先验融合思路。
原文依据 jointly learns a global gravity-alignment prior inferred from dense monocular perspective fields, and a planar alignment prior
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论空地视觉定位是一项具有挑战性的任务，因为两个视角之间在场景尺度与视点上存在显著差异。本文探索联合学习相机标定与鸟瞰图（BEV）投影的实际收益，用于在未标定的空中与地面视角之间估计完整的六自由度相对相机姿态。我们提出视觉几何对齐（VGA），一个统一框架，它联合学习由稠密单目透视场推断出的全局重力对齐先验，以及通过在共享 BEV 平面上进行 Procrustes 对齐来补全未观测方位角的平面对齐先验。在推理阶段，我们通过整合预测的逐相机重力对齐与相对平面方位角来联合优化相对相机姿态，从而在极端大基线与有限重叠条件下，从视觉输入中获得更好的朝向与平移对齐。我们在具有挑战性的 MatrixCity、ACC-NVS1 与 ULTRRA 空地图像对上评估了本方法，结果表明，利用学习到的几何先验进行优化可以进一步提升跨不同高度与环境的相机姿态估计。
英文原文摘要 1184 字符
Aerial-ground visual localization is a challenging task due to the significant differences in scene scale and view point captured between two views. In this work, we explore the practical benefit of jointly learning camera calibration and bird's-eye-view (BEV) projection for estimating full 6 Degrees-of-freedom relative camera pose between uncalibrated aerial and ground views. We present Visual Geometry Alignment (VGA), a unified framework that jointly learns a global gravity-alignment prior inferred from dense monocular perspective fields, and a planar alignment prior complementing the unobserved azimuth angle through Procrustes alignment in a shared BEV plane. At inference, we jointly refine the relative camera pose by integrating the predicted per-camera gravity alignment and relative planar azimuth angle, yielding improved orientation and translation alignment from visual input with extreme wide base-lines and limited overlap. We evaluate our method on challenging MatrixCity, ACC-NVS1 and ULTRRA ground-aerial pairs, demonstrating that optimizing with learned geometric priors can further improve the camera pose estimation across diverse altitudes and environment.