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.
Yes: This transformer will process input groups in order. Changes on 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.
No: This is the default behavior. Processing will only occur in this transformer once all input is present.
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.
This transformer accepts multiple input point clouds and rasters. One point cloud is output for each input point cloud.
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
Search FME Knowledge Center
Search for samples and information about this transformer on the FME Knowledge Center.
Tags Keywords: point "point cloud" cloud PointCloud coerce LiDAR sonar expose extract extents orthophotos PointCloudOnRasterValueExtractor