Try a Real-World Sample Dataset

High-resolution panorama data, exactly as delivered by the service.
No signup required beyond an email address.

What’s included in the sample dataset

This sample 16K panorama dataset is from a real Matterport project and demonstrates how the delivered panorama data can be used outside the Matterport platform.

A real-world delivery created with the Panorama Service.
It allows you to evaluate image quality, leveling accuracy, spatial alignment, and overall usability before placing an order. 

  • 16K image-leveled equirectangular panoramas and positioning metadata

  • 8K panoramas with orientation and positioning metadata

  • 4K cubefaces (as captured)

  • All data provided as standard image files 

The files embedded metadata (spatial positioning and/or leveling) work with many professional Apps. 


  

Example: From MP panorama files to a working 3DVista tour 

The video below demonstrates how the sample data can be used to create a complete 3DVista tour in just a few steps.

 

Some processing steps are shown shortened for demonstration purposes.

  • Measure distance between two scan points in Matterport
  • Import the panoramas you received from the panorama service
  • Apply Gyroscope Data to all panoramas
  • Enter Distance (measured in step 1)
  • Select all panoramas
  • Use “Autoconnect” to create yo tour tour, mark "Use embedded GPS coordinates if available"
  • That's it!

 

Download the sample dataset

Enter your email address to receive the download link for the complete sample dataset. 

  • 16K leveled panoramas

  • 8K metadata-aligned panoramas

  • 4K cubefaces

  • reference Matterport tour link

Get Sample Dataset

 

Who this sample is intended for

This sample is intended for professionals who work with high-resolution 360° imagery and need full control over their panorama data outside the capture platform.

  • AEC documentation and archiving

  • Virtual tour creation with third-party tools

  • Independent long-term storage

  • Evaluation before ordering a full dataset

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