Files
Introduction
The PointCloudToPointCoercer is used when writing to formats that do not support point clouds. It will transform the point cloud into either a multipoint feature, an aggregate of points, or individual points.
Transformer Highlights
There are 5 options for output geometry with the PointCloudToPointCoercer:
- Spatial Equal Points
- Sequential Equal Points
- Nested Equal Points
- Single Multipoint
- Individual Points
Spatial, Sequential and Nested Equal Points can produce several multipoint outputs. This is demonstrated using colors in the examples below. For each of these output geometries, the maximum number of points per multipoint was set to 1,000 (left), 10,000 (middle), and 100,000 (right) to show how results may vary. The total number of points in the point cloud is divided by the value set in the maximum points per multipoint parameter to determine how many multipoint features will be made.
Running the Workspace
The attached workspace demonstrates the differences in results between the methods described below. If you choose to run all methods at once, the workspace will take a very long time to finish running (approximately 8 minutes). The PointCloudToPointCoercer used to generate Individual Points will take the most time because millions of points are written individually. To run the methods separately, objects within bookmarks can be enabled or disabled. Simply right-click on a bookmark and select Disable or Enable All Objects in Bookmark.
To view the results of the PointCloudToPointCoercer, you may connect inspectors to the FeatureColorSetter transformers as seen below in image a, or turn on Feature Caching before running the workflow as seen below in image b. This will allow you to view the results in the Data Inspector.
a. b.
1. Spatial Equal Points
Spatial mode attempts to split the point cloud into multipoint features, each with approximately the same number of points.
2. Sequential Equal Points
Sequential mode splits the incoming point cloud sequentially, that is, forming a chunk as soon as the number of points reaches a specified threshold. In the point cloud, points are not necessarily ordered in a reasonable spatial order, so the chunks in sequential mode can look quite random and vary significantly from one point cloud to another.
3. Nested Equal Points
Nested mode attempts to create multipoint aggregates that are approximately the same size in terms of dimensions. This produces a tiled-like output of aggregates.
4. Single Multipoint
The single multipoint option converts the entire point cloud into a single multipoint aggregate.
5. Individual Points
The individual points mode will convert each point from the LAS point cloud into a single point feature. Since point clouds are large and can have millions of points, this conversion can be quite slow.
Please visit the PointCloudToPointCoercer documentation page to learn more.
GeometryCoercer vs. PointCloudToPointCoercer
Both the GeometryCoercer and PointCloudToPointCoercer are similar in that they can change the geometry type of the input feature. For this reason, it may seem unclear how the outputs may differ when changing the geometry type of a point cloud.
The major distinction between the two transformers is that the PointCloudToPointCoercer can retain components of the point cloud in the multipoint output, whereas the GeometryCoercer cannot. This is shown in the image below. The parameters for the PointCloudToPointCoercer were adjusted to preserve the components: intensity, classification, and scan_direction. In the feature information, this information is retained. In the GeometryCoercer, this information cannot be stored.
Please visit the GeometryCoercer documentation page to learn more.
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.