PointCloudCombiner

This transformer accepts all geometries. Area-based geometries will be converted to point clouds according to the spacing parameter.

A single point cloud feature.

Use this parameter to organize point clouds into groups. Each group of point clouds will have its own output point cloud.

Note: How parallel processing works with FME: see About Parallel Processing for detailed information.

This parameter determines whether or not the transformer should perform the work across parallel processes. If it is enabled, a process will be launched for each group specified by the Group By parameter.

Parallel Processing Levels

For example, on a quad-core machine, minimal parallelism will result in two simultaneous FME processes. Extreme parallelism on an 8-core machine would result in 16 simultaneous processes.

You can experiment with this feature and view the information in the Windows Task Manager and the Workbench Log window.

No: This is the default behavior. Processing will only occur in this transformer once all input is present.

By Group: This transformer will process input groups in order. Changes of the value of the Group By parameter on the input stream will trigger batch processing on the currently accumulating group. This will improve overall speed if groups are large/complex, but could cause undesired behavior if input groups are not truly ordered. Specifically, on a two input-port transformer, "in order" means that an entire group must reach both ports before the next group reaches either port, for the transformer to work as expected. This may take careful consideration in a workspace, and should not be confused with both port's input streams being ordered individually, but not synchronously.

Considerations for Using Input is Ordered By

Using Ordered input can provide performance gains in some scenarios, however, it is not always preferable, or even possible. Consider the following when using it, with both one- and two-input transformers.

Single Datasets/Feature Types: Are generally the optimal candidates for Ordered processing. If you know that the dataset is correctly ordered by the Group By attribute, using Input is Ordered By can improve performance, depending on the size and complexity of the data.

If the input is coming from a database, using ORDER BY in a SQL statement to have the database pre-order the data can be an extremely effective way to improve performance. Consider using a Database Readers with a SQL statement, or the SQLCreator transformer.

Multiple Datasets/Feature Types: Since all features matching a Group By value need to arrive before any features (of any feature type or dataset) belonging to the next group, using Ordering with multiple feature types is more complicated than processing a single feature type.

Multiple feature types and features from multiple datasets will not generally naturally occur in the correct order.

One approach is to send all features through a Sorter, sorting on the expected Group By attribute. The Sorter is a feature-holding transformer, collecting all input features, performing the sort, and then releasing them all. They can then be sent through an appropriate filter (TestFilter, AttributeFilter, GeometryFilter, or others), which are not feature-holding, and will release the features one at a time to the transformer using Input is Ordered By, now in the expected order.

The processing overhead of sorting and filtering may negate the performance gains you will get from using Input is Ordered By. In this case, using Group By without using Input is Ordered By may be the equivalent and simpler approach.

In all cases when using Input is Ordered By, if you are not sure that the incoming features are properly ordered, they should be sorted (if a single feature type), or sorted and then filtered (for more than one feature or geometry type).

As with many scenarios, testing different approaches in your workspace with your data is the only definitive way to identify performance gains.

If this parameter is set to Yes, the attributes from the original features will be merged onto the output point cloud features.

If you specify a Count Attribute parameter (text string), an attribute with this name will be added to each output feature.

The attribute contains the number of features that were combined to create the point cloud feature.

This parameter specifies whether points should be created for nodata cells in rasters. When set to Yes, a point will be created for every cell in a raster, regardless of whether it is nodata. When set to No, points will not be created for nodata cells. A cell is considered to be nodata when, for each selected band, the value for that cell is equal to that band's nodata value. If any cell value is not equal to that band's nodata value, the cell will be considered data.

This parameter controls the spacing between points in ground coordinates that will be used to generate the representative point grid used for surfaces, solids, polygons, and donuts. You can either enter a number or extract the value from a selected attribute.

Specifies the measures on input vector geometries that should be preserved as components in the output point cloud. The type of the output component must also be specified.

Specifies the attributes on input features that should be preserved as components in the output point cloud. The type of the output component must also be specified. Note that if the same component is specified as both a measure and attribute to preserve, the measure value will be preferred.