Setting a Point Cloud’s Intensity Component in FME

Liz Sanderson
Liz Sanderson
  • Updated

FME Version

  • FME 2022.0

Introduction

This article demonstrates how to set or correct a point cloud’s intensity component. The previous article in this series demonstrated how to set the colour components, and the next article demonstrates how to set the classification.

To understand the basics of working with point clouds in FME, see Getting Started with Point Clouds and Point Clouds (IFMEPointCloud) documentation. Key transformers in this scenario include the PointCloudOnRasterComponentSetter, which sets a point cloud’s component values based on an overlaid raster, and the PointCloudExpressionEvaluator, which sets component values based on expressions or conditional statements.
 

Step-by-Step Instructions

The goal of this scenario is to highlight a specific geographical feature in a point cloud by making it brighter. To do this, you will need a corresponding dataset containing the geometry of interest.

We will highlight roads by setting custom intensity component values on those points. Our source data includes a point cloud in LAS format and an Esri Shapefile containing road geometries.

0 - Source data.png
Source data. Left: LAS point cloud. Right: Shapefile road data.

In the last tutorial in this series, we used a raster to colorize the point cloud. In this tutorial, we will create our own raster to set the point cloud’s intensity values, which we will apply using the PointCloudOnRasterComponentSetter.

1. Open the starting workspace in FME Workbench
Download the tutorial attachments, and open ChangingPointCloudIntensity_Start.fmwt to follow along. The workspace has two readers to get us started with the source data: a LAS point cloud and an Esri Shapefile.

1 readers.png

2. Add a Clipper
First, clip the Shapefile to the area of interest: the extents of the point cloud. Add a Clipper transformer to the canvas. Connect the LAS reader feature type to the “Clipper” port, and the Shapefile reader feature type to the “Candidate” (formerly “Clippee”) port.

2 clipper.png

This transformer clips the road network so it looks like this:

2 - clipper 1 output.PNG

3. Add a Bufferer
Next, we will make the road corridors more visible. Add a Bufferer transformer, and connect the “Inside” port from the Clipper to its input.

3 bufferer.png

Open the parameters, and set the Buffer Distance to 40. Under “Area Parameters”, set “Dissolve on Attributes” to fme_feature_type.

After passing through this transformer, the data looks like this:

3 bufferer output.png

4. Add a 3DForcer
We will now set the intensity of the road corridors so they are more visible in the resulting point cloud. Add a 3DForcer transformer, and connect it after the Bufferer.

4 3DForcer.png

Open the parameters, and set the Elevation to 10. This Z value will become the intensity.

4 3DForcer params.png

After passing through this transformer, the road network will be given a Z value (note how it's 3D when inspected in Visual Preview):

4 3DForcer output.png
5. Add a NumericRasterizer
Add a NumericRasterizer transformer, and connect it after the 3DForcer. This will create a raster using the Z coordinates from the previous step.

5 NumericRasterizer.png

Open the parameters, and under “Raster Properties”, set both the Number of Columns and Number of Rows to 5000. Set the Interpretation Type to UInt8.

5 NumericRasterizer params.png

This transformer will output a numeric raster that we can overlay on the point cloud in order to set the intensity.

6. Add a PointCloudOnRasterComponentSetter
To overlay the point cloud and the newly created raster, add a PointCloudOnRasterComponentSetter transformer. Connect the LAS reader to “Point Cloud”, and the NumericRasterizer to “Raster”.

6 PCORCS.png

Open the parameters, and set “Components to Set” to “Custom”. Add one band as follows:

  • Band: 0
  • Component: intensity
  • Default value: 9

6 PCORCS params.png

7. Add an Inspector
Connect the “PointCloud” output port to an Inspector, which will route the output to Visual Preview so we can view it.

7 Inspector.png

8. Run the workspace
Run the workspace to view the new point cloud with emphasized roads. The result should look like the image on the right:

output.png
The output. Left: original point cloud. Right: the point cloud with increased intensity values on the road network.
 

Data Attribution

The data from these exercises originates from data made available by the Ohio Geographically Referenced Information Program. The GIS Support Center maintains enterprise and site licenses for commercial data sets that are supportive of the Ohio Enterprise.

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