10.12.2014 - Dietmar Funck
Classification of 3D Point Clouds
3D point clouds are a representation of surfaces, describing them as a discrete set of points. There are different remote sensing techniques available for the creation of 3D point clouds (e.g., laser scanning or image matching). The structure and the available point attributes of the 3D point cloud might be influenced by the used remote sensing technique.
3D point clouds are used in several applications, such as urban and landscape planning, building reconstruction or documentation of sites or vegetation.
Usually, 3D point clouds are only used as input data for these applications to derive further information or representations, such as 3D models. To derive such data and for further analysis, applications often use only points belonging to a specific object class (e.g., ground, building, vegetation). However, semantic information, such as the object class of a point, is not available after the data capturing.
Therefore, classification approaches are used to derive semantic information based on the structure of the 3D point cloud and available point attributes. Usually, classification approaches make use of segmentation approaches to group points in homogeneous areas together.
The talk will present a classification approach which adapts to the average point density and available point attributes.