Thinning and Combining Point Clouds

Introduction

In this article, we will focus on two transformers: the PointCloudCombiner and the PointCloudThinner. The PointCloudCombiner can be used with both point clouds and other file formats to output a new point cloud. The PointCloudThinner is useful for removing points in order to reduce the file size of the output point cloud. Below are three examples that demonstrate how these transformers can be used together or independently in FME Workbench.

Video

This video was recorded using FME 2015.1. The interface may be different, but the concepts are the same.  

Files

Due to the large file size of the point clouds, each dataset must be downloaded separately from the City of Vancouver's Open Data Portal. Optionally, you can copy and paste the FTP URLs directly into the LAS reader, as shown in Part 1 (Alternate Method).

Data:

Due to the large file size of the point clouds, each dataset must be downloaded separately from the City of Vancouver's Open Data Portal. Optionally, you can copy and paste the URLs directly into the LAS reader, as shown in Part 1 Step 1.

https://webtransfer.vancouver.ca/opendata/2013LiDAR/COV_4910E_54570N.zip

https://webtransfer.vancouver.ca/opendata/2013LiDAR/COV_4900E_54570N.zip

https://webtransfer.vancouver.ca/opendata/2013LiDAR/COV_4900E_54580N.zip

https://webtransfer.vancouver.ca/opendata/2013LiDAR/COV_4910E_54580N.zip

Workspaces:

CombiningPointClouds-2026.zip Contains Part 1, 2, and 3 workspaces as well as the data for Part 3.

Step-by-Step Instructions

Part 1: Combining Point Clouds

The PointCloudCombiner transformer can be used to combine multiple point clouds that may be tiled or overlapping into a single point cloud. To build this example, follow the steps below:

1. Add a LAS Reader

In FME Workbench, start with a blank workspace and click the Reader button in the toolbar. In the Add Reader dialog, set the following parameters:

• Format: ASPRS Lidar Data Exchange Format (LAS)
• Dataset: “/COV_4890E_54570N.zip”, “/COV_4890E_54580N.zip”, “/COV_4900E_54570N.zip”, “/COV_4900E_54580N.zip”
  • Click the ellipsis to browse to the downloaded dataset, hold Shift on your keyboard, and select all four zipped .las files

Click OK to add the reader to the workspace.

If you run the workspace and inspect the output of the reader feature type, you will see four separate datasets. We will need to combine these into a single document.

(Alternate Method) Use HTTPS URLs in the Reader

If you prefer not to download the data, as the file sizes are large, you can use the HTTPS URLs directly within the reader. In the LAS reader, for the dataset, copy and paste each HTTPS URL separated by a comma, and then click OK to add the reader. Ensure that you run the workspace before trying to view the data. When using HTTPS URLs, it is a good idea to use feature caching so the data doesn't need to be pulled from the internet each time.

Paste the following into the reader Dataset to use all four URLs:

https://safe-kc.s3.us-west-2.amazonaws.com/Thinning+and+Combining+Point+Clouds/COV_4900E_54570N.zip,
https://safe-kc.s3.us-west-2.amazonaws.com/Thinning+and+Combining+Point+Clouds/COV_4900E_54580N.zip,
https://safe-kc.s3.us-west-2.amazonaws.com/Thinning+and+Combining+Point+Clouds/COV_4910E_54570N.zip,
https://safe-kc.s3.us-west-2.amazonaws.com/Thinning+and+Combining+Point+Clouds/COV_4910E_54580N.zip

When pasting the above text, remove the quotations “” from the start and end.

2. Add a PointCloudCombiner

If we were to add a LAS writer to write the files out directly without using a transformer, we would end up with four separate output files. To join them into a single file, we need to use the PointCloudCombiner.

Add a PointCloudCombiner transformer to the canvas and connect its Input port to the LAS reader feature type. 

There are numerous parameters that can be adjusted in the PointCloudCombiner, but since we are simply combining points, we can leave the parameters at their defaults.

3. Add a LAS Writer

Now we can write out the combined point cloud. Click the writer button in the Toolbar and set the following parameters:

• Format: ASPRS Lidar Data Exchange Format (LAS)
• Dataset: /Output
  • Browse to a location to save the dataset
• LAS File Definition: Automatic

 OK to add the writer.

In the Feature Type dialog, set the LAS File Name:

• LAS File Name: Vancouver-Downtown

Click OK.

4. Run the Workspace

Connect the PointCloudCombiner Output port to the Vancouver-Downtown writer feature type. 

Once connected, run the workspace. Once the workspace has finished running, click on the Vancouver-Downtown writer feature type to open the mini-toolbar. On the mini-toolbar, click View Written Data to view the data in Data Preview.

In the Select Dataset to View dialog, click the ellipsis, browse to your output location, and select the output Vancouver-Downtown.las file to view.

We can see that the four LAS files have been joined into a single file.

You can confirm this by looking at the output folder. There should only be a single Vancouver-Downtown.las file.

Save the workspace as it will be used in the next example.

Part 2: Thinning Point Clouds

Continuing from the first example, the point clouds can be thinned before they are combined to produce a point cloud with fewer points and ultimately a smaller file.

1. Open the Workspace from Part 1

Continuing in the workspace from part one, we will thin the point clouds to reduce our file size. When running the workspace before adding the PointCloudThinner, there are 77,908,980 points in the point cloud, and the file size is 2180 MB, which takes about 15 seconds to run. We don’t need to keep the detailed point cloud so we can remove some of the points.

2. Add a PointCloudThinner

Add a PointCloudThinner to the canvas and connect its Input port to the LAS reader feature type. Connect the PointCloudThinner Thinned output port to the PointCloudCombiner Input port:

Open the PointCloudThinner parameters to set the interval:

• Interval: 5

This will keep every fifth point.

3. Run the Workspace

Now, run the workspace again, and take note of how much faster the workspace runs. This time it only took 10 seconds, and we now have 15,581,794 Points points with a file size of 436 MB. If you view the point cloud before and after thinning, it looks slightly different, but all of the key details are there.

Workspace runtime will vary depending on the computer.

Part 3: Combining a Raster DEM into a PointCloud

The PointCloudCombiner can also combine other kinds of geometries, such as rasters, polygons, and even surfaces, and output them as a point cloud. For this example, we will read in a raster digital elevation model (DEM) and transform it into a point cloud.

1. Open FME Workbench and Add a Reader

In a blank FME workspace, add a reader from the Toolbar and set the following parameters:

• Format: Canadian Digital Elevation Data (CDED)
• Dataset: /DEM-Clipped.dem
  • Browse to downloaded dataset

Click OK to add the reader.

Click on the CDED reader feature type to open the mini toolbar, then click on the View Source Data button to view the data in Data Preview.

Here, we have a digital elevation model of Vancouver, BC.

2. Add a PointCloudCombiner

Now, add a PointCloudCombiner transformer to the canvas and connect its Input port to the CDED reader feature type. 

By default, the PointCloudCombiner transforms the raster pixel location into the x/y component and the pixel value into the z component of the resulting point cloud.

3. Add a LAS Writer

Now we can write out the newly created point cloud. Add a writer to the canva with the following parameters set:

• Format: ASPRS Lidar Data Exchange Format (LAS) 
• Dataset: /Output
  • Browse to a location to save the dataset 
• LAS File Definition: Automatic

Click OK. In the Feature Type dialog, change the LAS File Name:

• LAS File Name: Vancouver

Click OK to add the writer to the canvas.

4. Run the Workspace

Connect the PointCloudCombiner Output port to the Vancouver writer feature type. Then, run the workspace and inspect the output.

Remember to select the specific file (Vancouver.las) in the Select Dataset to View dialog.

In the Data Preview window, we can see that a point cloud has been created.

Additional Resources

PointCloudCombiner Transformer Documentation

PointCloudThinner Transformer Documentation

Using the PointCloudFilter Article

Data Attribution

The data used here originates from open data made available by the City of Vancouver, British Columbia. It contains information licensed under the Open Government License - Vancouver.