Reduces the number of points in a point cloud by selectively keeping points based on the shape of the point cloud. The simplified and removed points are output as two discrete point clouds.

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Typical Uses

  • Reducing the data volume of a point cloud feature to meet processing or storage requirements, when honoring the overall shape of the original dataset is desirable.

How does it work?

The PointCloudSimplifier receives point cloud features and outputs them with fewer points than the original. The points to keep are identified by an algorithm that determines the overall shape of the point cloud, and then selectively discards points.

Areas with high rates of change (such as steep slopes) will have more points kept, and areas with low rates of change (generally flat areas) will be thinned more aggressively. This is based on the Medial Axis Transform (MAT) of the original, a representation like a skeleton of the entire point cloud feature. Individual points are considered against the MAT and evaluated for inclusion or exclusion.

The manner of generation of the MAT, method of sampling, and desired level of simplification can be optionally adjusted through parameters.

Both the simplified point cloud and a new point cloud feature containing all removed points are output.

Note  Medial Axis Transform (MAT) Simplification. This method estimates normals for each point and uses a ball-shrinking algorithm to approximate the MAT. Then it samples the point cloud based on each point’s Local Feature Size (LFS), defined by the shortest distance from the point to the medial axis. Points in areas of high curvature (low LFS) are sampled at a higher density than redundant low curvature (high LFS) points.


Usage Notes

  • This transformer is very processing intensive. The PointCloudThinner also reduces the number of points in a point cloud, using either regular sampling intervals or first/last x points. It is much faster than the PointCloudSimplifier, but does not consider the shape of the feature.

Choosing a Point Cloud Transformer

FME has a selection of transformers for working specifically with point cloud data.

For information on point cloud geometry and properties, see Point Clouds (IFMEPointCloud).


Input Ports

Output Ports


Editing Transformer Parameters

Using a set of menu options, transformer parameters can be assigned by referencing other elements in the workspace. More advanced functions, such as an advanced editor and an arithmetic editor, are also available in some transformers. To access a menu of these options, click beside the applicable parameter. For more information, see Transformer Parameter Menu Options.

Defining Values

There are several ways to define a value for use in a Transformer. The simplest is to simply type in a value or string, which can include functions of various types such as attribute references, math and string functions, and workspace parameters. There are a number of tools and shortcuts that can assist in constructing values, generally available from the drop-down context menu adjacent to the value field.

Dialog Options - Tables

Transformers with table-style parameters have additional tools for populating and manipulating values.


Processing Behavior


Feature Holding


Dependencies None

FME Community

The FME Community is the place for demos, how-tos, articles, FAQs, and more. Get answers to your questions, learn from other users, and suggest, vote, and comment on new features.

Search for all results about the PointCloudSimplifier on the FME Community.


Examples may contain information licensed under the Open Government Licence – Vancouver and/or the Open Government Licence – Canada.

Keywords: point "point cloud" cloud PointCloud LiDAR sonar