Point filtering and clustering

Points Filtering and Clustering

This category of tools includes functions dedicated to work with point clouds, to enable other processes and further results. Most of these tools work on any point clouds, some of them work only on grid point clouds.

The filtering tools include also the point cloud Pre-process filters: Reconstructor® applies a set of algorithms to the scans which extract information that is needed during further processing of the data.

All the commands can be activate also through the point clouds contextual menu.

Processes			On Grid Point Clouds	On Unstructured Point Clouds
Filtering	Pre-process clouds	Noise Removal (Range & Reflectance Gate, Outlier Removal, Median Filter, Mixed Point Filter, Noise Remover) Compute Normals Edge Detection (Depth & Orientation Discontinuity) Compute Confidence	✓
	Pre-process clouds	Noise Removal (Outlier Removal, Noise Remover) Compute Normals Compute Confidence		✓
	Restore raw data	To undo any operation of preprocessing, deletion and editing that may have been performed on the clouds.	✓	✓
	Restore deleted points	To undelete all the points earlier deleted.	✓	✓
	Edit 2D	A grid point cloud is shown in its 2D representation. Here you can select, delete and undelete points with several functions.	✓
	Fill holes	To replace any invalid point in the cloud with a value averaged from the point's neighbourhood in the cloud's structure.	✓
	Hide black points	To invalidate all the points in the cloud that are colored in full black.	✓	✓
	Remove duplicated points	To invalidate any point that has exactly the same coordinates of another point in the cloud.	✓	✓
	Resample	To resample a point cloud, subsampling it.	✓	✓
	Simplify points	To determine the most relevant points from a point of view of shape description, and save them into the new unstructured point cloud. These resulting clouds work as compact representations of the original structured ones.	✓
	Extract edges	To extract the edges of a grid point cloud, in form of polylines.	✓
	Level 3D density of clouds	To cluster clouds excluding duplicated or unneeded points. The resulting cloud, however, will not contain all points from the input clouds, but only those needed to guarantee a fixed 3D density of the points.	✓	✓
Clustering	Level 3D density of clouds	To cluster clouds excluding duplicated or unneeded points. The resulting cloud, however, will not contain all points from the input clouds, but only those needed to guarantee a fixed 3D density of the points.	✓	✓
Clustering	Make single cloud	To lump together in an unstructured point cloud an arbitrary set of point clouds.	✓	✓
Virtual scan		To resample the scene and generate a new clustered grid point cloud.	✓	✓

See Cloud Processing for details.