Cintoo Cloud

Category: Reality Capture
Works With: Docs
What the Cintoo Cloud Integration Does
  • Share and distribute the laser scan data with whoever via the cloud with no compromise on accuracy
  • View, annotate, measure the laser scan data from anywhere at any time in a web browser
  • Compare the as-built (laser scans) and the design intent (3D BIM) during construction in a browser
  • Distribute the scan data to whoever via the cloud as RCS or E57 files for Scan-to-BIM
Integration Details

Cintoo Cloud transforms each scan position into a 3D mesh that is more than 20 times smaller than the point cloud. The inverse transformation is used to distribute the data as RCS files into Autodesk portfolio. 3D BIM model can be overlaid over the scan data in the viewer to check for differences.

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Quick and Accessible Point Clouds
What do you like best?
The ability to quickly and efficiently access point clouds helps increase the speed of our workflow
What do you dislike?
We recently started using it and havent run into any negatives
What problems are you solving with the product? What benefits have you realized?
Large point clouds which were previous slow to.access are now quick and with tools we can get the data we need
We use for point cloud and model coordination
What do you like best?
Web based software we can use on any device. Customer service is also great!
What do you dislike?
Station based VR experience doesn't allow fly through experience
What problems are you solving with the product? What benefits have you realized?
Gives us a platform to share with our clients, large point cloud meshing online
About Cintoo

Cintoo develops Cintoo Cloud to turn massive terrestrial laser scan data into cloud and BIM compatible Reality Data that can be shared, viewed, annotated, measured or distributed for Scan-to-BIM workflows in a collaborative way. Cintoo Cloud also enables Scan-vs-BIM workflow for QA / QC process.

Learn More About This Integration

Cintoo Cloud White Paper describes the technology behind the platform. With the point cloud-to-mesh transformation, the performance relates to file size reduction before upload to the cloud (20 to 30 times smaller). With the inverse transformation of meshes-to-point cloud, it relates to the sub-millimeter accuracy of the reconstructed point cloud from the mesh.