CloudClean
Last updated
Last updated
CloudClean is used to make a smart-decimated cloud, or to increase the precision on surfaces. Smart decimation refers to reducing the density of a pointcloud by removing points furthest from estimated surfaces in the cloud.
Users have several options for improving precision on hard surfaces. CloudClean decimated will attempt to improve precision without reducing density, similar to Statistical Outlier Removal (SOR). The primary difference between the two tools is that SOR will result in a changed classification (assigning points to noise class), whereas CloudClean will create an entirely new pointcloud. For a more arbitrary form of decimation, configure the lidar processing settings to exclude every other line, or as is the case with multi-laser systems, utilize auto-downsampling.
Output Mode: Choose between created Decimated, Projected, or both clouds
Points per Square Meter: Target point density for the output cloud. Used only with Decimated mode.
Uniformity: A value of 1 will try to maximize uniform density in the pointcloud. A value of 0 will disregard uniformity and focus only on improving surface precision. Most users will want to use the default value of 0.
Decimated: Given point density, algorithm picks points that are nearest to the computed surface. It then keeps original points from the cloud, but discards the points furthest away from computed surface.
Projected: Projects every original point to a computed surface. It keeps all the points from the cloud, but moves or 'projects' them closer to the computed surface.
