A point cloud filtering method based on anisotropic error model

Abstract

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 Moiré 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.

Modeling the Global Relationship via the Point Cloud Transformer for the Terrain Filtering of Airborne LiDAR Data

“…Digital Elevation Models (DEMs) are digital models used to describe the height and shape of a terrain surface and have been widely used in various fields such as surveying, hydrology, meteorology, geological hazards [1,2], soil, engineering construction, and communication [3][4][5]. Airborne LiDAR [6], as one of the main methods for obtaining DEMs [7][8][9], has become irreplaceable in obtaining large-scale and high-precision geographical scene data due to its high efficiency, strong penetration, and other characteristics. However, in LiDAR data, filtering out non-ground points is crucial for building a DEM.…”