PointCloudSimplifier

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.

Examples

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).

Point Cloud Transformers

PointCloudCombiner	Combines features into a single point cloud. Point cloud and non-point cloud geometries are supported.
PointCloudComponentAdder	Adds new components with constant values to a point cloud.
PointCloudComponentCopier	Copies selected component values onto either a new or existing component
PointCloudComponentKeeper	Keeps only specified point cloud components, discarding all others.
PointCloudComponentRemover	Removes specified components from a point cloud.
PointCloudComponentRenamer	Renames an existing component.
PointCloudComponentTypeCoercer	Alters the data type of point cloud components, and converts component values if required.
PointCloudConsumer	Reads point cloud features for testing purposes, including any accumulated point cloud operations. No additional operations are performed, and nothing is done with the features.
PointCloudCreator	Creates a point cloud of specified size and density, with default component values.
PointCloudExpressionEvaluator	Evaluates expressions on each point in a point cloud feature, including algebraic operations and conditional statements, and sets individual point cloud component values.
PointCloudExtractor	Serializes the geometry of a point cloud feature into a Blob attribute, encoding the contents according to a choice of common binary point cloud formats.
PointCloudFilter	Separates point clouds into multiple features, based on evaluating expressions including component values, and creates a separate output port for each expression defined.
PointCloudMerger	Merges point clouds by joining points where selected component values match (join key), including x, y, z, and other components. Component values are transferred between point clouds and output is filtered based on matching success and duplication.
PointCloudOnRasterComponentSetter	Sets point cloud component values by overlaying a point cloud on a raster. The component values for each point are interpolated from band values at the point location.
PointCloudPropertyExtractor	Extracts the geometry properties of a point cloud feature and exposes them as attributes, optionally checking for their existence, retrieving component properties, and finding minimum and maximum values. Extents may also be recalculated and updated.
PointCloudReplacer	Decodes a binary attribute containing encoded point clouds stored as Blobs, replacing the feature’s geometry with the decoded point cloud.
PointCloudSimplifier	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.
PointCloudSorter	Sorts the points within a point cloud by one or more component values.
PointCloudSplitter	Separates point clouds into multiple features based on component values, color, or first/last return.
PointCloudStatisticsCalculator	Calculates statistics on point cloud components and adds the results as attributes.
PointCloudSurfaceBuilder	Takes an input point cloud and reconstructs it into an output mesh.
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.
PointCloudToPointCoercer	Converts point clouds to point or multipoint geometries, optionally retaining attribute and component values.
PointCloudTransformationApplier	Applies a point cloud’s scale, offset, or transformation matrix to it, recalculating component values and removing the transformation values.

Configuration

Input Ports

Output Ports

Parameters

Medial Axis Transform Parameters

Filter Ratio

Specifies the desired level of simplification, relates LFS values to sampling density. Higher values will result in lower overall density.

Typical values range from 0.01 to 0.8.

Minimum Sampling Density

Specifies the minimum sampling density for grid cells (in points per square unit).

A value of 0 means that the sampling density is not bounded from below. This can be useful to maintain a minimum level of detail in nearly flat regions which would otherwise be removed entirely because of plane detection.

Maximum Sampling Density

Specifies the maximum sampling density for grid cells (in points per square unit).

A value of 0 means that sampling density is not bounded from above.

LFS Based Simplification

Linear: Use the unmodified LFS values for each point during simplification.

Quadratic (LFS^2): Use the square of the LFS value for each point during simplification. This will increase the effect that curvature has on point sampling.

Tuning and Noise Reduction

Grid Cell Size	Specifies the cell size for grid-based LFS simplification. During simplification, the point cloud is divided into a uniform grid of this size. The sampling density is calculated for each cell based on the average LFS values of its contained points. This should be as small as possible, but large enough to have an average of 5-10 points per cell. Auto: Let FME estimate a reasonable cell size based on the bounding box and number of points in the input point cloud.
Initial Ball Radius	Specifies the initial radius used by the ball-shrinking algorithm. Larger values will allow for the detection of larger shapes when approximating the MAT. Auto: Let FME estimate a reasonable initial radius based on the bounding box and number of points in the input point cloud.
Normal Estimation Neighbors	Specifies the number of nearby points to consider when estimating point normals. Use higher values with denser inputs to produce more consistent normals. Typical values range from 5 to 20.
Ball Preservation Threshold	Specifies the minimum angle (in degrees) between the defining points for a medial ball during a ball-shrinking iteration. Iteration stops when the angle would fall below this threshold. A value of 0 means that no ball preservation takes place. Typical values range from 0 to 30.
Plane Detection Threshold	Specifies the minimum angle (in degrees) between the defining points for the initial medial ball. If the angle on the first iteration is below this threshold then no medial ball is assigned. A value of 0 means that no plane detection takes place. Typical values range from 0 to 40.

Advanced

Bisector Neighbors

Specifies the number of nearby points to check against the Bisector Threshold. Higher values will result in fewer noisy MAT points but will also limit the ability to detect small features.

Typical values range from 1 to 5.

Bisector Threshold

The Bisector Threshold is used to clean the MAT points before LFS computation. Lower values mean more aggressive cleaning which means more robustness to noise in the MAT, but fewer features will be detected.

Typical values range from 0.1 to 10.

LFS Computation

Use Interior MAT Only: Only consider the interior MAT when computing LFS values.

Use Interior and Exterior MAT: Consider both the interior and exterior MAT when computing LFS values.

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.

How to Set Parameter Values

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.

Using the Text Editor

The Text Editor provides a convenient way to construct text strings (including regular expressions) from various data sources, such as attributes, parameters, and constants, where the result is used directly inside a parameter.

Text Editor

Using the Arithmetic Editor

The Arithmetic Editor provides a convenient way to construct math expressions from various data sources, such as attributes, parameters, and feature functions, where the result is used directly inside a parameter.

Arithmetic Editor

Conditional Values

Set values depending on one or more test conditions that either pass or fail.

Parameter Condition Definition Dialog

Content

Expressions and strings can include a number of functions, characters, parameters, and more.

When setting values - whether entered directly in a parameter or constructed using one of the editors - strings and expressions containing String, Math, Date/Time or FME Feature Functions will have those functions evaluated. Therefore, the names of these functions (in the form @<function_name>) should not be used as literal string values.

Content Types

String Functions	These functions manipulate and format strings.
Special Characters	A set of control characters is available in the Text Editor.
Math Functions	Math functions are available in both editors.
Date/Time Functions	Date and time functions are available in the Text Editor.
Math Operators	These operators are available in the Arithmetic Editor.
FME Feature Functions	These return primarily feature-specific values.
FME Parameters	FME and workspace-specific parameters may be used.
Creating and Modifying User Parameters	Create your own editable parameters.

Dialog Options - Tables

Table Tools

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

Row Reordering

Enabled once you have clicked on a row item. Choices include:

Add a row
Remove a row
Move current row up one
Move current row down one
Move current row to top
Move current row to bottom

Cut, Copy, and Paste

Enabled once you have clicked on a row item. Choices include:

Cut a row - delete and copy to clipboard
Copy a row to the clipboard
Paste a row from the clipboard

Cut, copy, and paste may be used within a transformer, or between transformers.

Filter

Start typing a string, and the matrix will only display rows matching those characters. Searches all columns. This only affects the display of attributes within the transformer - it does not alter which attributes are output.

Import

Import populates the table with a set of new attributes read from a dataset. Specific application varies between transformers.

Reset/Refresh

Generally resets the table to its initial state, and may provide additional options to remove invalid entries. Behavior varies between transformers.

Note: Not all tools are available in all transformers.

For more information, see Transformer Parameter Menu Options.

Reference

Processing Behavior	Feature-Based
Feature Holding	No
Dependencies	None
Aliases
History

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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