  A novel multi-scale operator for unorganized 3D point clouds is introduced.
The Difference of Normals (DoN) provides a computationally efficient,
multi-scale approach to processing large unorganized 3D point clouds. The
application of DoN in the multi-scale filtering of two different real-world
outdoor urban LIDAR scene datasets is quantitatively and qualitatively
demonstrated. In both datasets the DoN operator is shown to segment large 3D
point clouds into scale-salient clusters, such as cars, people, and lamp posts
towards applications in semi-automatic annotation, and as a pre-processing step
in automatic object recognition. The application of the operator to
segmentation is evaluated on a large public dataset of outdoor LIDAR scenes
with ground truth annotations.
