Dense Structure Inference for Object Classification in Aerial LIDAR Dataset

We present a framework to classify small freeform objects in 3D aerial scans of a large urban area. The system first identifies large structures such as the ground surface and roofs of buildings densely built in the scene, by fitting planar patches and grouping adjacent patches similar in pose together. Then, it segments initial object candidates which represent the visible surface of an object using the identified structures. To deal with sparse density in points representing each candidate, we also propose a novel method to infer a dense 3D structure from the given sparse and noisy points without any meshes and iterations. To label object candidates, we build a tree-structure database of object classes, which captures latent patterns in shape of 3D objects in a hierarchical manner. We demonstrate our system on the aerial LIDAR dataset acquired from a few square kilometers of Ottawa.