FME Transformers: 2024.1

Categories
Point Clouds
Related Transformers
Generalizer
PointCloudSimplifier
Sampler

PointCloudThinner

Reduces the number of points in (thins) a point cloud by keeping points at a fixed interval, a maximum number of points, or a set quantity of first or last points. Remaining points are discarded.

Jump to Configuration

Typical Uses

  • Reducing the data volume of a point cloud feature to meet processing or storage requirements.
  • Creating a sample point cloud dataset for testing

How does it work?

The PointCloudThinner receives point cloud features and outputs them with fewer points than the original.

The Points to Keep are identified by a fixed interval, a maximum number of points, or by specifying a first or last number of points:

Thinning Type Amount Result

Every Nth Point (Interval)

5

Every 5th point of the input cloud feature will be present in the output point cloud.

Every Nth Point (Maximum Number of Points)

100

The output cloud will have a maximum of 100 points, evenly spaced throughout the input.

First N Points

100

The first 100 points from the input will be kept.

Last N Points

500

The last 500 points from the input will be kept.

Examples

Usage Notes

  • For finer control over how a point cloud is reduced, consider using the PointCloudSimplifier transformer. It reduces while maintaining the overall shape of the original point cloud feature, however, may take considerably more processing time.

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).

Configuration

Input Ports

Output Ports

Parameters

Editing Transformer Parameters

Transformer parameters can be set by directly entering values, using expressions, or referencing other elements in the workspace such as attribute values or user parameters. Various editors and context menus are available to assist. To see what is available, click beside the applicable parameter.

For more information, see Transformer Parameter Menu Options.

Reference

Processing Behavior

Feature-Based

Feature Holding

No

Dependencies None
Aliases PointCloudSampler
History  

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 PointCloudThinner on the FME Community.

 

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

Keywords: point "point cloud" cloud PointCloud LiDAR sonar