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There are many transformations that can be used with point cloud datasets to create sensible transformations with reasonable outputs. FME is "smart" enough to "understand" what is a correct transformation in each particular case. Listed below are transformers that are commonly used with LiDAR datasets. The links provided will lead to articles with scenarios that show how these transformers may be used.
There are several specific point cloud transformers available in FME. These transformers either manipulate point clouds without changing geometry, make point clouds from other geometries, or make other geometries from point clouds.
Point clouds can be manipulated in many different ways with FME. The PointCloudThinner and PointCloudSimplifier both remove points in systematic ways which can reduce file size while the PointCloudCreator is used to add points. The PointCloudSplitter and PointCloudFilter can transform the LAS data into multiple point clouds. More complex transformations can be done with PointCloudTransformationApplier or the PointCloudSurfaceBuilder.
Many changes can be made to the components (attributes) of a point cloud dataset. There are multiple transformers available for these changes such as the PointCloudComponentAdder, PointCloudComponentRemover or the PointCloudComponentTypeCorercer.
Information about the point cloud dataset can be found using transformers like the PointCloudStatisticsCalculator, PointCloudConsumer or PointCloudPropertyExtractor. Transformers like the PointCloudReplacer allow you to change information about the point cloud dataset relative to an additional “blob” dataset.
Point cloud datasets may have their orientation manipulated using transformers like the Rotator, Scaler and Tiler.
The TINGenerator, DEMGenerator, SurfaceModeller and SurfaceDraper construct delaunay triangulation based on input LAS points and breaklines before further manipulation to create 3D terrain or building models. The Extruder transformer may be used in models to create solid geometries like buildings.
Use transformers like ImageRasterizer and NumericRasterizer to draw input point, line and polygon features onto a color raster to visualize the point cloud as an area with complete coverage. The rasters can further be used to create 3D models like DEM’s, DTM’s, or TIN’s.
The BoundsExtractor and CoordinateExtractor transformers will add the maximum and minimum coordinate values or specific coordinates as attributes to the point cloud at the specified vertex of the bounding box.
Simple adding or removal of points in a point cloud can be done with transformers like the Bufferer and Clipper. These transformers are useful when converting certain parts of your point cloud data into other data formats.
See more available transformers in the FME Transformer Gallery.
If you think that some transformer should work differently with point cloud data, please let us know here.
Calculating Point Cloud Density
PointCloudSplitter: Splitting Point Clouds by Components
Clipping and Tiling Point Cloud Data
Creating Rasters and DEMs from Point Clouds
Tutorial: Getting Started with Point Clouds
Add Color to Point Clouds|PointCloudOnRasterComponentSetter
Converting Point Clouds to Surface Models without Classification
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