We introduce Paired-CSLiDAR (CSLiDAR), a cross-source aerial-ground LiDAR benchmark for single-scan pose refinement: refining a ground-scan pose within a 50 m-radius aerial crop. The benchmark contains 12,683 ground-aerial pairs across 6 evaluation sites and per-scan reference 6-DoF alignments for sub-meter root-mean-square error (RMSE) evaluation. Because aerial scans capture rooftops and canopy while ground scans capture facades and under-canopy, the two modalities share only a fraction of their geometry, primarily the terrain surface, causing standard registration methods and learned correspondence models to converge to metrically incorrect local minima. We propose Residual-Guided Stratified Registration (RGSR), a training-free, geometry-only refinement pipeline that exploits the shared ground plane through height-stratified ICP, reversed registration directions, and confidence-gated accept-if-better selection. RGSR achieves 86.0% [email protected] m and 99.8% [email protected] m on the primary benchmark of 9,012 scans, outperforming both the confidence-gated cascade at 83.7% and GeoTransformer at 76.3%. We validate RMSE-based pose selection with independent survey control and trajectory consistency, and show that added Fourier-Mellin BEV proposals can reduce RMSE while increasing actual pose error under extreme partial overlap. The dataset and code are being prepared for public release.

翻译：我们提出Paired-CSLiDAR（CSLiDAR）——一种面向单次扫描位姿精化的跨源空地LiDAR基准数据集，旨在将地面扫描位姿精化至半径50米的航空场景范围内。该基准涵盖6个评估地点的12,683组地面-航空配对数据，并配备单次扫描的6自由度参考对齐，支持亚米级均方根误差（RMSE）评估。由于航空扫描捕捉屋顶与冠层，而地面扫描捕捉立面与冠下结构，两种模态仅共享部分几何特征（主要为地表），导致常规配准方法及学习型对应模型易收敛至度量错误的局部极小值。为此，我们提出残差引导分层配准（RGSR）——一种无需训练的纯几何精化流程，通过高度分层的ICP、反向配准方向及置信门控的择优接受策略，充分利用共享地平面。在包含9,012次扫描的主基准上，RGSR达到86.0%的[email protected]与99.8%的[email protected]，优于置信门控级联法（83.7%）和GeoTransformer（76.3%）。我们通过独立测量控制点与轨迹一致性验证了基于RMSE的位姿选择，并证明在极端部分重叠场景下，引入傅里叶-梅林BEV提议虽可能增加实际位姿误差，但可降低RMSE。数据集与代码即将公开发布。