Tranning: Cognite Advanced 3D Model Contextualization

Develop an advanced training program to equip users with skills for contextualizing point cloud data, focusing on both detected and undetected objects. The training should address gaps in traditional modeling approaches by providing practical, hands-on experience with diverse scenarios.
 

Challenges Addressed:

• Limited automation in object detection, requiring significant manual effort.
• Difficulty in contextualizing objects that remain undetected in raw point cloud data.
• Inability to handle diverse and complex industrial scenarios effectively.

Hands-On Examples and Exercises:

• Detected Objects:

  • Import and preprocess a point cloud dataset.
  • Use AI-driven tools to identify and classify detected objects.
  • Automatically link detected objects to an asset hierarchy, metadata, or P&ID diagrams.
     

• Undetected Objects:

  • Demonstrate manual workflows for identifying undetected objects within the point cloud.
  • Tag, classify, and link undetected objects using the training interface.
  • Show how to create relationships between manually contextualized objects and other datasets

Use examples from different industries (oil & gas, manufacturing, energy, etc.) to cover various asset types.
Include examples of objects partially occluded, poorly defined, or from atypical asset classes.

 

Resource Materials:

• Develop a library of sample datasets, best practices, and case studies for ongoing reference.