A point cloud filtering method based on anisotropic error model

Many modelling applications require 3D meshes that should be generated from filtered/cleaned point clouds. This paper proposes a methodology for filtering of terrestrial laser scanner (TLS)-derived point clouds, consisting of two main parts: an anisotropic point error model and the subsequent decimation steps for elimination of low-quality points. The point error model can compute the positional quality of any point in the form of error ellipsoids. It is formulated as a function of the angular/mechanical stability, sensor-to-object distance, laser beam's incidence angle and surface reflectivity, which are the most dominant error sources. In a block of several co-registered point clouds, some parts of the target object are sampled by multiple scans with different positional quality patterns. This situation results in redundant data. The proposed decimation steps removes this redundancy by selecting only the points with the highest positional quality. Finally, the Good, Bad, and the Better algorithm, based on the ray-tracing concept, was developed to remove the remaining redundancy due to the Moire effects. The resulting point cloud consists of only the points with the highest positional quality while reducing the number of points by factor 10. This novel approach resulted in final surface meshes that are accurate, contain predefined level of random errors and require almost no manual intervention.

Automated summary

This paper proposes a methodology for generating 3D meshes from filtered/cleaned point clouds, consisting of two main parts: an anisotropic point error model and subsequent decimation steps. The point error model calculates the positional quality of each point and is based on factors such as stability, distance, incidence angle, and reflectivity. The decimation steps remove redundant data and the Good, Bad, and the Better algorithm is used to eliminate remaining redundancy due to Moire effects.