Distinctive 2D and 3D features for automated large-scale scene analysis in urban areas
We present a new methodology for large-scale urban 3D point cloud classification. We analyze a strategy for recovering individual 3D neighborhoods of optimal size. Our methodology involves efficient feature extraction and classification. Our methodology contains an extension towards data-intensive processing. We evaluate our methodology on two recent, publicly available point cloud datasets.
feature large-scale urban point cloud classification 3d scene analysis