LiDAR Self-Learn Starter (1 of 2)

I decided to write up this post because many students have had asked how they can start learning and visualizing LiDAR data. LiDAR data is becoming more and more available to the public and many want to learn about it.

Software: CloudCompare

According to their website, CloudCompare is “free to use them for any purpose, including commercially or for education”. This is good because if you are just trying to learn and wanted to make some maps, this is a good starting point without paying for it.

CloudCompare will read and write the following file formats, personally I have had to dealt with *.las format in the past. *.laz is the “zipped” version of *.las.

Modified from:

I wanted to go over few of the most common functions that I use

1. Cleaning

Let’s first talk about the interface, you can click to the “Properties” tab to look at the properties of the las file. Then, you can change the colorization according to the attribute that come with the file, here I change the colorization to “classification” and so 1) Tree points are yellow 2) Ground points are green and 3) small buildings are orange.

Note that there is a bounding box around the size of the file and the box is bigger than it should. This is because there are a lot of “noise” points in the air, there are not a lot of them so you cannot see them very well but looking at the bounding box, it is bigger than it should indicating that there are probably noise above the trees.

You can clean them by

•Tools -> SOR Filter •

Tools -> Noise Filter •

2. Distances

According to, the tool compute the distance comparison between two sets of point clouds


Using their example, two sets of points are being compared and the blue area has very little distance differences (two point clouds are almost the same there) and red areas have large differences (two point clouds are most different there). This tool is useful in change detection as well, imagine you acquire one set of LiDAR points from summer and one set in winter times on top of forested area, you will be able to detect all the missing tree crowns due to leaves fallen on the ground.

CloudCompare also lets you do the following comparisons which I often use:

•Cloud-to-cloud (C2C)

•Cloud-to-mesh (C2M)

•Cloud-to-primitive (C2P)

•Robust cloud-to-cloud (M3C2) – Using ‘clean’ normals is very important in M3C2

3. Rigid Transformation Matrices

This is a useful tool if you want to simply apply a transformation matrix to a set of point cloud.

‘Edit -> Apply Transformation’ tool
You can even apply the inverse transformation by checking the corresponding checkbox (e.g. to go back to the previous position of the entity)

In this example, I am applying a translation of 100 in x-direction.

4. Alignment

Alignment is used when I want to align a set of point cloud with respect to another one, or some known points (reference points – think ground control points GCPs for referencing an image).

This can be done three ways:

•Match bounding-box centers – simply select two (or more) entities, then call ‘Tools > Register > Match bounding-box centers’

•Manual transformation •’Edit > Rotate/Translate’ menu

•Picking point pairs – Tools > Registration > Align (point pairs picking). This is the most useful one, you have to decide which one is reference and which one is the one you want to align. Then, pick the point pairs (at least 3 pairs). Click “Align” button and you will also get RMS results for evaluation.

In this example provided from CloudCompare:

“As soon as you have selected at least 3 or more pairs (you must have picked exactly the same number of points in each cloud) CC will display the resulting RMS and you can preview the result with the ‘align’ button. You’ll also see the error contribution for each pair next to each point in the table (so as to remove and pick again the worst pairs for instance). You can add new points to both sets anytime (even after pressing the ‘align’ button) in order to add more constraints and get a more reliable result. And as suggested before, you can also remove points with the dedicated icons above each tables or with the ‘X’ icon next to each point.”

Also, if you have a reference point in 3D that you already know (e.g. by ground survey, or corners of the building) you may use them as reference and enter those points manually for alignment.

5. Automatic Registration

This two register two sets of point clouds automatically, using ICP (Iterative Closest Point)

  • Tool > Fine Registration (ICP)
  • There are three common parameters you may want to adjust
  1. Number of iterations/RMS difference – ICP is an iterative process-Stop this process either after a maximum number of iterations or as soon as the error (RMS) lower than a given threshold
  2. Final overlap – user specify the actual portion of the data/registered cloud that would actually overlap the model/reference cloud if both clouds were registered.
  3. Adjust Scale – determine a potential difference in scaling. If your clouds have different scales (e.g. photogrammetry clouds) you can check this option so as to resolve the scaling as well

The rest is pretty straight forward.

In this example provided by CloudCompare, the final transformation matrix can be calculated with RMS produced automatically. Theorical overlap is 50% which means the two sets of point cloud overlap by 50%. I found this is an important parameter to set in obtaining a “good” RMS.

These are my five top picks for CloudCompare, stay tuned for self-learn part 2.

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