Viewing Point Clouds

Sample Datasets:

4MB

737_Public.csv

56MB

WayzataBayPublic.csv

CloudCompare Import

1. Export your sonar data from SonarView as a CSV (or use one of the samples)

2. Open CloudCompare

3. Drag the CSV from your file explorer onto CloudCompare's 3D viewport (the main part of the window). A dialog will appear

4. To view the data in CloudCompare, the fields must be assigned correctly as shown below. Note: changing 'Skip Lines' will clear any columns you have assigned, so make that change first.

Setting	Value
Skip Lines	1
Column 3	coord.Z
Column 4	coord.X
Column 5	coord.Y

Click Apply to import the point cloud. You should see something like this;

The color of the points is determined by the ping number. In order to color the points by their height, we need to generate a scalar field containing the height of the point. This can be accomplished in two ways:

1. Open your CSV in a spreadsheet program (Excel, LibreOffice Calc), copy column 3 (Z position), paste it as a new column (after the ping number column), save the CSV, and then import to Cloud Compare as described above. Designate your new column as a scalar field. After importing, you will be able to use the scalar field to color the points.

2. Use CloudCompare's rasterize tool to generate a new point cloud with the scalar field.

   1. Open the Rasterize tool

      

   2. Set the following settings:

      1. Choose step size (in meters), this will vary depending on your needs, for this example 0.2m is used.

      2. Set the active layer to Z Values.

      3. Direction: Z

      4. Cell height: Minimum

      5. Project SFs: minimum value.

      6. The Fill With setting depends on your needs. Leave Empty will most accurately represent the original point cloud. Kriging will generate new points to fill gaps, which can produce a satisfying result.

   

   
3. Click Export -> Cloud to save the new point cloud, which will be automatically colored by height.

Working with Point Clouds

The point cloud can be made more readable and visually appealing by changing the color scale of the new point cloud. The Viridis color scale is used in this image.

Increasing the point size can make it easier to see the points when zoomed in:

Trimming the display range (of the color scale) can help emphasize a feature: