Contextualize point cloud
You can contextualize point clouds in two ways: by manually creating each object you want to contextualize or by leveraging the point cloud's segmentation. You must use both services to achieve a fully contextualized point cloud.
Before you start
Make sure you have already uploaded the point cloud you want to contextualize. For more information on uploading point clouds, see Upload point cloud.
The following section will explain how to contextualize point clouds.
Get started
Before contextualizing, you should filter and index the point cloud to generate tiles that you can use for object detection and accelerate the contextualization process.
It is an automated process triggered by selecting the filter icon in your model's action column.
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Under Actions, select
...
andFiltering and indexing.
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Select the check box to generate tiles.
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Select OK and the process starts.
Once the filtering and indexing are processed, trigger the object detection. The Run classification icon will now be enabled.
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Select Run classification to start the point cloud object detection.
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Enable both Classification and Pipe detection.
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Select Run.
Classification will perform semantic segmentation of the point cloud, while pipe detection will detect segments in the point clouds that resemble pipes.
Once an object is detected, you will see an orange tag on the right, suggested annotations indicating how many objects were detected.