Paired-CSLiDAR: Height-Stratified Registration for Cross-Source Aerial-Ground LiDAR Pose Refinement
Montana Hoover, Jing Liang, Tianrui Guan, Dinesh Manocha

TL;DR
This paper presents a new cross-source LiDAR benchmark and a geometry-only refinement method, RGSR, that significantly improves pose accuracy in aerial-ground scans with minimal overlap.
Contribution
The introduction of the CSLiDAR benchmark and the Residual-Guided Stratified Registration (RGSR) method for pose refinement in cross-source aerial-ground LiDAR data.
Findings
RGSR achieves 86.0% success at 0.75 m threshold
RGSR outperforms confidence-gated cascade and GeoTransformer
Fourier-Mellin proposals can reduce RMSE in extreme cases
Abstract
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…
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