  LiDAR point clouds provide rich geometric information, which is particularly
useful for the analysis of complex scenes of urban regions. Finding structural
and semantic differences between two different three-dimensional point clouds,
say, of the same region but acquired at different time instances is an
important problem. A comparison of point clouds involves computationally
expensive registration and segmentation. We are interested in capturing the
relative differences in the geometric uncertainty and semantic content of the
point cloud without the registration process. Hence, we propose an
orientation-invariant geometric signature of the point cloud, which integrates
its probabilistic geometric and semantic classifications. We study different
properties of the geometric signature, which are an image-based encoding of
geometric uncertainty and semantic content. We explore different metrics to
determine differences between these signatures, which in turn compare point
clouds without performing point-to-point registration. Our results show that
the differences in the signatures corroborate with the geometric and semantic
differences of the point clouds.
