Visually-complete aerial LiDAR point cloud rendering

Aerial LiDAR (Light Detection and Ranging) point clouds are gathered by a downward scanning laser on a low-flying aircraft. Due to the imaging process, vertical surface features such as building walls, and ground areas under tree canopies are totally or partially occluded, resulting in gaps and sparsely sampled areas. These gaps produce unwanted holes and uneven point distributions that often produce artifacts when visualized using point-based rendering (PBR) techniques. We show how to extend PBR by inferring the physical nature of LiDAR points for visual realism and added comprehension. More specifically, the class of object a point is related to augments the point cloud in pre-processing and/or adapts the online rendering, to produce visualizations that are more complete and realistic. We provide examples of point cloud augmentation for building walls and ground areas under tree canopies. We show how different types of procedurally generated geometry can be used to recover building walls. These methods are generic and can be applied to any aerial LiDAR data set with buildings and trees. Our work also incorporates an out-of-core strategy for hierarchical data management and GPU-accelerated PBR with extended deferred shading. The combined system provides interactive visually-complete rendering of virtually unlimited-size LiDAR point clouds. Experimental results show that our rendering approach adds only a slight overhead to PBR and provides comparable visual cues to visualizations generated by off-line pre-computation of 3D polygonal urban models.