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3D contextualization connects 3D models (CAD, point clouds, and 360° images) to assets so you can navigate between geometry and asset data. The APIs you use depend on your CDF project type:
  • Asset-centric (legacy): assets are asset-centric, and contextualization approvals are typically stored via the Annotations API (for 360° images) or asset-centric 3D APIs.
  • Hybrid: you can contextualize both asset-centric assets and core data model assets. Approvals are stored using 3D asset mappings (CAD) and the Annotations API (360° images and point clouds).
  • Data modeling: assets are core data model instances (for example, CogniteAsset). Contextualization annotations are stored in Cognite data modeling, but you create them through the 3D contextualization APIs. For point clouds, 3D jobs can generate contextualization suggestions.
Asset-centric contextualization approvals are deprecated. That includes 3D asset mappings to asset-centric assets and approvals stored with the Annotations API. In a data modeling project, use the 3D contextualization APIs on this page. These endpoints are in public preview. The Python SDK does not include these endpoints yet, so call them over HTTP. 3D model upload stays available. See Deprecated and retired features and Data modeling status for CDF projects.

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Suggestions vs. approvals

It helps to separate contextualization into two stages:
  • Suggestions: automated output you review (for example, point cloud volumes or detected text regions in 360° images).
  • Approvals: the links you create to store the contextualization (for example, “this 360° text region refers to this asset”).

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Which API to use

The table below summarizes the main API entry points for 3D contextualization.
For 360° text suggestions in asset-centric and hybrid projects, the Vision API is deprecated. We recommend upgrading to a data modeling project.
Last modified on October 15, 2026