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This function allows the user to detect

clusters of points that are close to each other and

group them together.

Normally in a point

cloud the points are linked together by something, for example the ground, the floor or the walls in the case of a building scan.

 By isolating these points from the point cloud it will be possible to detect clusters of points.

There are a few steps to follow before being able to detect clusters. The user must first detect the ground points (or the floor points – or the wall points), send the points to a specific class and hide these points by unchecking them in the class list. Once these points are hidden, there should be unconnected clusters of points. These are the objects that will be detected with this function.

Hiding the ground emphasizes groups of points. For example

, cars, road signs, utility poles, trees, people, buildings are all objects that can be singled out once the ground is hidden.

The documentation for this tool is split into separate categories:

• Cluster library:

•  location where the user decides to

• store the

• clusters
• Detection parameters: explanations for the different

• detection settings
• Using the clusterization tool: how to correctly run a cluster detection
• Cluster categories and filters: how to filter clusters properly and create cluster categories
• Classifying clusters by type: using the cluster tool to classify clusters