Editing Point Cloud Data

Gemini Terrain offers several specialized functions for processing and editing point cloud data. These functions make it possible to streamline work with large point clouds by reducing the data volume or editing points based on various criteria.

Point Cloud Functionality

When you have selected a point cloud in the drawing, you can right-click to access specific point cloud functions.

Available functions:

1. Reduce number of points
2. Extract points along line
3. Remove points along line
4. Edit point cloud along line
5. Edit point cloud in 3D

Note

Some options are only available in the context menu if a line is also included in the selection.

Reduce Number of Points

The program has functions for processing point clouds, removing gross errors, reducing the number of points based on cell size, and removing points in flat areas.

Noise Removal

This function is recommended for use on flat surfaces, such as scanned structures. The function works less optimally on uneven surfaces like rough rock cuts.

The function determines a plane that best fits the point selection. The Number of neighbors field defines how many points to include at a time.

Figure: A = Point of interest (green), B = Distance to the plane (dotted line). The figure shows the best-fit plane through 6 (blue) neighboring points.

When all distances are calculated, an analysis is performed to find the standard deviation of the calculated distances. The threshold for point removal is set to standard deviation × sigma level. All points with a distance greater than this threshold are classified as noise and removed.

You can choose to preserve the removed points as a separate point cloud. This option is enabled by default, and the point cloud is placed on the same layer as the result.

Example

If the standard deviation is calculated as 0.05 m, and you use a sigma level = 6, the threshold becomes 0.3 m.

Tip

It is recommended to use a sigma value of 6 or higher. Lower values may cause points in areas with "sharp edges" to be incorrectly identified as noise and removed.

Note that a repeated operation on the resulting point cloud with the same parameters will likely remove additional points. This occurs because a new and smaller standard deviation is calculated, resulting in a lower threshold for removal.

If desired, the process can be repeated until all noise points are removed.

Reducing by Cell Size

This option can be used to reduce the number of points in a point cloud. Based on the specified cell size (bounding box), the program selects a point based on the chosen method to represent the cell.

Example

The example below shows the use of the Average method:

Original point cloud from ground scan where density is greater the closer you are to the scanner. Density varies from 0.01 to 0.1 meters.

Resulting point cloud after reduction with a cell size of 0.1 meters.

Note

The function is based exclusively on geometric considerations of proximity between points. It does not take special consideration for edge areas or sharp breaks in the data.

The algorithm finds the nearest points within the defined cell size and calculates an average value (x,y,z) for these points.

Removing Points in Flat Areas

This function works opposite to Noise Removal. Points are removed when the deviation is less than the threshold (standard deviation × sigma level).

Example

The function is particularly useful in connection with terrain interventions.

Point cloud before removal of points in flat areas.

Point cloud after removal of points in flat areas - only significant height variations are preserved.

Result Layer

You can choose where the result should be stored. By default, the program suggests the active layer.

Point clouds in the result layer are given properties from the processing:

Attribute	Code	Description
S_JOBRESULT	S20 Noise removed (Point cloud where noise has been removed)	Code values indicating how the point cloud was created and what it contains. S20 and S21 are generated when checking Remove noise, S22 when checking Reduce by cell size
	S21 Removed noise points (Point cloud with points classified as noise)
	S22 Simplified for 3D cell size
Proc_SigmaLevel		Input standard deviation sigma level
Proc_StdDev		Standard deviation for the calculation
Proc_CellSize		Cell size for Reduce by cell size