Pointcloud Task
A Pointcloud Task prepares 3D point clouds for visualization in the VC Map. Depending on the selected mode (Conversion, Change Detection, or Colorization), additional attributes such as color values or distance information are generated.
Supported input formats are LAS and LAZ. Other point cloud formats such as XYZ, PCD, PNTS, or E57 must be converted to LAS or LAZ before processing.
| A valid pointcloud license is required to execute point cloud tasks. Point cloud data sources that have already been published can also be displayed in VC Map without a license. |
Step 1
| Option | Description |
|---|---|
Task Name |
Name of the task. |
Create or overwrite Datasource |
Select whether to create a new datasource or overwrite an existing one |
Datasource Name |
If an existing data source is to be overwritten, select the source here. |
Step 2 - Conversion Modes
Classical Conversion
In the classical conversion, point clouds in LAS format are converted into the Cesium format to make them available for visualization in the VC Map.
| Setting | Description |
|---|---|
Path to input data |
Path to the directory containing the input data. |
EPSG code of input data |
Point cloud projection. If it differs from the project projection, the point cloud is internally reprojected. |
Change Detection
In the Change Detection, a comparison is performed between a base dataset and a reference dataset, calculating a distance value for each point. The base dataset is converted into the Cesium format and supplemented with the DISTANCE attribute, enabling declarative styling in the VC Map.
| Setting | Description |
|---|---|
Path to base data |
Path to the directory containing an older dataset used for comparsion. |
Path to reference data |
Path to the directory containing a newer dataset. |
EPSG code of input data |
EPSG code of the input data. The correct code must be specified for both datasets. |
Colorization
In Colorization, point clouds in LAS format are converted into the Cesium format and additionally colored using orthophotos to enable realistic visualization in VC Map.
| Setting | Description |
|---|---|
Path to input data |
Path to the directory containing the input data. |
EPSG code of input data |
Point cloud projection. If it differs from the project projection, the point cloud is internally reprojected. |
Path to source images |
Path to the directory containing the source images (*.jpg, *.jp2, *.tif) of the orthophotos used to colorize the point clouds. The images must be georeferenced and cover the entire point cloud. |
Advanced Settings (optional)
The following settings apply to all Pointcloud processing modes.
| Setting | Description |
|---|---|
Number of threads |
Number of CPU threads allowed for the conversion. Ensure that your system retains sufficient resources for other background processes during job execution. |
Converter Memory |
Amount of memory in megabytes (MB) that the publisher may use for the point cloud job. Allocating more memory can reduce processing time. Ensure that sufficient memory remains available for other system processes. |
Additional optional parameters (space-separated) |
Allows specifying additional input parameters for the job. Optional parameters must be entered with two leading hyphens and separated by spaces. |
Styling Notes
Visualization in VC Map depends on available point attributes in the datasource.
| Styling Mode | Required Attribute |
|---|---|
RGB visualization |
RGB |
Classification mapping |
SemanticClass |
Change-based visualization |
DISTANCE |
Declarative Style Example
Example: You can colorize classification results by referencing the SemanticClass attribute within a declarative layer style. To configure this:
-
Open the App Configurator.
-
Navigate to Layer Styles.
-
Select Add Declarative Style.
-
Open the JSON Editor.
-
Define your colorization rules using the SemanticClass attribute.
This allows the visualization to automatically apply distinct colors to features based on their assigned semantic class.
{
"type": "DeclarativeStyleItem",
"name": "Classification Example",
"declarativeStyle": {
"show": true,
"color": {
"conditions": [
["${SEMANTIC} === 3", "color('#e53935')"],
["${SEMANTIC} === 4", "color('#43a047')"],
["${SEMANTIC} === 2", "color('#fdd835')"],
["true", "${COLOR}"]
]
},
"pointSize": 2
}
}
The attribute SemanticClass must be added to the datasource during job execution using:--attributes SemanticClass
|