Using Gradients And Tensor Voting In 3d Local Geometric Descriptors For Feature Detection In Airborne Lidar Point Clouds In Urban Regions

Structural or geometric classification of three-dimensional (3D) point clouds of urban regions from airborne LiDAR enables feature (object-based) classification, and 3D reconstruction. Here, we consider positive semidefinite symmetric second-order tensors as local geometric descriptors (LGDs), which gives structural classification. We compute LGDs using local neighborhood, and their eigenvalue-based features are conventionally used for object-based classification and 3D reconstruction. We combine derivative based 2D gradient energy tensor, and anisotropically diffused 3D voting tensor, using a multi-scale approach, to compute a LGD. We represent LGDs as second-order tensors, and compare the relevant eigenvalue-based features and saliency maps obtained from them. We visually compare the outcomes of our LGD with conventionally used covariance matrix.