ContextInsights

This page describes how to use the “ContextInsights” in Orbit.

ContextInsights requires a separate installation but does not require separate entitlement.
For ContextInsights installation guidelines, see Install & Configure ContextInsights 20.1 for Orbit.

Main Toolbar > Extensions > ContextInsights

ContextInsights extension enables automatic detection on mapping and point cloud resources.
It relies on detectors to identify objects or regions of interest.

Running Context Insights detectors requires Python, ContextInsights Engine, and original images or point cloud from any mapping resource. The results can be directly imported in the mapping resources via the Annotations procedure.

A ContextInsights job will leverage machine learning to execute different types of detection in your reality data.
There are various types of jobs available:

• Image 2D Objects: Will detect elements as regular 2D-bounding boxes in images. Regular 2D boxes around assets.
• Image 2D Segmentation: Will detect regions in images and deliver segmented raster. Regions to segmented raster.
• Point Cloud 3D Segmentation: Attribute a class to each point of laser scan data. Classify each element of a point cloud.

Each job type requires a suited detector to be executed on reality data. E.g: a 2D segmentation detector will only be used for image-based segmentation jobs

Special notes from Image-based jobs:

• Only original jpg images are used, optimized omi images are not supported today.
• A mask can be used to ignore image pixels from jobs. Place the image mask (TIF format) in <mapping resource>/<camera>/mask/mask.tif.

A detector is a frozen model that is pre-trained by Bentley Systems. Running on your reality data through a ContextInsights job, it will automatically recognize elements of interest. List of pre-trained detectors is available here:
https://communities.bentley.com/products/3d_imaging_and_point_cloud_software/w/wiki/54656/context-insights-detectors-download-page

A detector is specific to a certain job type. The quality of the detection will depend on the similarity between your dataset and the training dataset’s description.

Launch ContextInsights Engine

Configure the directory to the ContextInsights detectors. The detectors and specs are listed underneath.

Define whether you want to create a new job or open existing one

• Create a new job and enter the target location of the results.
• Open an existing job and browse to the location of the results.

Depending on the added detectors, the following annotation types will be available.

Delegate the process to the task manager or start now. Running the ContextInsights Engine is required before starting the process.

Open the Annotations procedure where the result xml file is already pre-filled as source object annotation file.
Import 2D Objects or 2D Segmentations, depending on the chosen Annotation Type.

Open the target directory to verify the results.

Import the scene xml file as 2D Object annotations to overlay the results on original or optimized images

Apply Segmentation at optimize imagery to display the result on optimized images