Typical Uses
- Performance testing or benchmarking point cloud processing.
- Debugging point cloud processing.
- Testing point cloud feature validity.
How does it work?
The PointCloudConsumer receives point cloud features and reads all of their data at their current state in the workspace. It does not perform any new operations on the point clouds.
FME handles point clouds in a “delayed evaluation” model. A point cloud reader reads essential information about a point cloud, but does not read the actual point contents until it is absolutely necessary. Transformer operations are accumulated until the results are needed, often being held until the features enter a writer. At that point, the required data is fully read and processed.
This is intended to optimize performance - for example, if a point cloud is both clipped and reprojected, FME will optimize processing by not reprojecting data that falls outside of the clip boundary and is ultimately discarded.
The PointCloudConsumer transformer forces this read to occur, and any accumulated operations are performed as the features are read. Nothing is done with the read features, and the transformer will not improve performance - it simply emulates the effect that reading the data would have, wherever it is placed.
It can be used to test workspace performance without configuring writers, and can also be useful for debugging point cloud processing workspaces with multiple accumulated operations. It may also be used for point cloud geometry validation, as it will reject any non-point cloud features.
Note: Though the transformer has a parameter that can be used to adjust the way in which the point cloud is read (Block Size), it is primarily of interest for internal Safe performance testing and is not generally useful in a production environment.
Examples
Example: Estimating point cloud processing time
In this example, we are building a point cloud processing workspace.
The source datasets are four LAS point clouds, totaling approximately 387 MB, which need to be reprojected, combined, have some component values calculated, and then be passed along for further processing.
As all of these operations will be accumulated until they are required (such as enter a writer), we cannot yet determine how long it might take to process this.
A run of the workspace as it stands is very fast, an indication that the point cloud operations have been held for later processing.
To get a more accurate idea of how long this will take, we add a PointCloudConsumer after the three point cloud processing transformers.
In the parameters dialog, we keep the default settings.
Running the workspace again, we can see that it will take more time to fully read and process the point clouds, with the accumulated operations applied.
Usage Notes
- The Enable Feature Caching option in FME Workbench should be disabled to obtain more accurate processing benchmarks.
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
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Combines features into a single point cloud. Point cloud and non-point cloud geometries are supported. |
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Adds new components with constant values to a point cloud. |
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Copies selected component values onto either a new or existing component |
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Keeps only specified point cloud components, discarding all others. |
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Removes specified components from a point cloud. |
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Renames an existing component. |
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Alters the data type of point cloud components, and converts component values if required. |
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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. |
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Creates a point cloud of specified size and density, with default component values. |
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Evaluates expressions on each point in a point cloud feature, including algebraic operations and conditional statements, and sets individual point cloud component values. |
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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. |
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Separates point clouds into multiple features, based on evaluating expressions including component values, and creates a separate output port for each expression defined. |
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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. |
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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. |
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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. |
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Decodes a binary attribute containing encoded point clouds stored as Blobs, replacing the feature’s geometry with the decoded point cloud. |
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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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Sorts the points within a point cloud by one or more component values. |
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Separates point clouds into multiple features based on component values, color, or first/last return. |
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Calculates statistics on point cloud components and adds the results as attributes. |
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Takes an input point cloud and reconstructs it into an output mesh. |
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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. |
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Converts point clouds to point or multipoint geometries, optionally retaining attribute and component values. |
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Applies a point cloud’s scale, offset, or transformation matrix to it, recalculating component values and removing the transformation values. |
Configuration
Input Ports
Input
This transformer accepts only point cloud features.
Output Ports
Output
Point cloud features with no modifications made.
<Rejected>
Non-point cloud features will be routed to the <Rejected> port, as well as invalid point clouds.
Rejected features will have an fme_rejection_code attribute with one of the following values:
INVALID_GEOMETRY_TYPE
Rejected Feature Handling: can be set to either terminate the translation or continue running when it encounters a rejected feature. This setting is available both as a default FME option and as a workspace parameter.
Parameters
General
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Block Size (Points) |
The point cloud will be consumed in blocks, reading this many points in each block. Adjusting this parameter is generally only useful for internal Safe testing. |
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.
How to Set Parameter Values
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.
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.
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
| These functions manipulate and format strings. | |
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Special Characters |
A set of control characters is available in the Text Editor. |
| Math functions are available in both editors. | |
| Date/Time Functions | Date and time functions are available in the Text Editor. |
| These operators are available in the Arithmetic Editor. | |
| These return primarily feature-specific values. | |
| FME and workspace-specific parameters may be used. | |
| Creating and Modifying User Parameters | Create your own editable parameters. |
Dialog Options - Tables
Transformers with table-style parameters have additional tools for populating and manipulating values.
Table Tools
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Row Reordering
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Enabled once you have clicked on a row item. Choices include:
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Cut, Copy, and Paste
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Enabled once you have clicked on a row item. Choices include:
Cut, copy, and paste may be used within a transformer, or between transformers. |
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Filter
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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. |
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Import
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Import populates the table with a set of new attributes read from a dataset. Specific application varies between transformers. |
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Reset/Refresh
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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.
Reference
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Processing Behavior |
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Feature Holding |
No |
| Dependencies | None |
| Aliases | |
| 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 PointCloudConsumer 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