Arithmetica announces new license options ahead of Pointfuse V3 launch

Shepperton Studios, London — Arithmetica, the company behind Pointfuse point cloud conversion software, announced a new monthly licensing option for Pointfuse V3, which will be released later this month. Suited toward project-specific use, Pointfuse now offers a monthly licensing option.

Pointfuse V3 will be available for download, with the new licensing option, starting Sept. 18 at www.pointfuse.com.

“Like the fruits of summer, Pointfuse V3 is well worth waiting for with selectable surfaces and reduced file sizes, as well as being simple to use,” commented Mark Senior, Business Development Manager at Arithmetica. “So, in order to make it more accessible and affordable to organizations who want to try it for the first time, or who may only need its advanced functionality on a project-by-project basis, we have introduced monthly licensing options.”

Pointfuse is a powerful modeling engine that delivers an automatic, precise and flexible way of converting the vast point cloud datasets generated by laser scanners or photogrammetry into segmented mesh models. Designed for anyone capturing or using point cloud data, Pointfuse uses advanced statistical techniques to create 3D models where individual surfaces can be selected and classified as new layers in the Pointfuse environment and exported to IFC and FBX for manipulation in any industry-standard CAD system.

Pointfuse V3 builds on the advances already seen in Pointfuse V2, which was launched in 2016. Offering “selectable surfaces,” Pointfuse V3 provides a unique approach, classifying objects within a 3D scene. Surfaces within the 3D mesh models produced by Pointfuse V3 can now be identified, grouped and classified. These advancements within Pointfuse V3 bring a catalyst to the workflow of design and engineering projects offering efficiencies that have not been possible when working with point clouds or traditional mesh models.

Pointfuse V3, significantly reduces the file size of 3D models created from point clouds. In simple terms, the data density within each surface has been reduced while still maintaining the fidelity of the model.  This results in a reduction in model size by a factor of 10, making ongoing use of the model easier, faster and more efficient.