For example, a potential use case is to colorize a point cloud using orthophotos.
Point cloud features are input through this port.
Raster features are input through this port.
Point cloud features with updated component values are output through this port.
If Group By attributes are selected, features with the same values in the Group By attributes are grouped together, and rasters are only used to set component values of point clouds in the same group.
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.
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.
Specifies which point cloud components should be set from the corresponding raster(s).
- Color specifies that the color_red, color_green, and color_blue components of a point cloud should be set. In this case, the input rasters should have three selected bands.
- Custom allows specification of an arbitrary list of components.
When specifying a custom list, the following values must be specified for each component:
- Band specifies the band index from which values will be taken.
- Component specifies the component whose values will be set.
- Default Value specifies the value that will be set for points that are disjoint from all rasters.
This parameter specifies the behavior when a point lies on a raster nodata value. If set to Yes, the point component value will be set to the nodata value. If set to No, the raster will be skipped, and the next raster checked. If no rasters are found to cover the point, then the Default Values Overwrite Data parameter determines the behavior.
This parameter specifies the behavior when no value can be found for a point from any raster, but the component already existed on the input point cloud. If set to Yes, the component value from the input point cloud will be preserved. If set to No, the component value will be set to the default value, either from the default value list or the FME default.
Cell values are interpolated to arrive at point cloud component values.
- Nearest Neighbor is the fastest but produces the poorest quality.
- Bilinear provides a reasonable intermediate option.
- Bicubic is the slowest but produces the best quality.
- Average 4 and Average 16 have a performance similar to Bilinear and are useful for numeric rasters such as DEMs.
The component value for each point is taken from the first raster that can supply one (for example, if a point does not overlap a raster, the next raster will be tried), so the order of input for rasters can impact the result.
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.
FME Licensing Level
FME Professional edition and above
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Tags Keywords: point "point cloud" cloud PointCloud coerce LiDAR sonar expose extract extents orthophotos PointCloudOnRasterValueExtractor