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LiDAR point clouds provide rich geometric information, which is particularlyuseful for the analysis of complex scenes of urban regions. Finding structuraland semantic differences between two different three-dimensional point clouds,say, of the same region but acquired at different time instances is animportant problem. A comparison of point clouds involves computationallyexpensive registration and segmentation. We are interested in capturing therelative differences in the geometric uncertainty and semantic content of thepoint cloud without the registration process. Hence, we propose anorientation-invariant geometric signature of the point cloud, which integratesits probabilistic geometric and semantic classifications. We study differentproperties of the geometric signature, which are an image-based encoding ofgeometric uncertainty and semantic content. We explore different metrics todetermine differences between these signatures, which in turn compare pointclouds without performing point-to-point registration. Our results show thatthe differences in the signatures corroborate with the geometric and semanticdifferences of the point clouds.

Augmented Semantic Signatures of Airborne LiDAR Point Clouds for Comparison