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Abstract |
In the last several years, airborne topographic light detection and ranging (LiDAR) has emerged as an important remote-sensing technology supporting a wide variety of applications. In that time, researchers have conducted studies to determine the optimal sampling densities required to support their respective applications. This natural progression of experimentation and analysis has resulted in several recommended sampling density stratifications for LiDAR point cloud products. Recognizing the need for point clouds of varying sample densities provides at least two motivations for creating level of details (LOD) for high-resolution point clouds. First, from the perspective of LiDAR data consumers, there is a desire to use the coarsest sampling that supports the application to reduce procurement costs, storage constraints, and processing times. Second, from the perspective of LiDAR data providers, there is a desire to collect data once at the highest supported fidelity to minimize recollection costs and redundancy in data holdings. In this article, we present an approach for generating point cloud LODs by constraining samples to regular discrete lattices to optimize the coverage of the volumes represented by each sample. We compare our approach to the two most common point cloud sampling methods: random sampling and rectangular lattice sampling. We discuss two approaches for representing the point cloud LODs. Finally, we propose an extension of our sampling approach for processing single-photon and Geiger-mode avalanche photodiode (GmAPD) LiDAR. |
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