Pointfuse Unlocks ‘As Built’ Data for BIM with Autodesk Integration
Maidenhead, UK, 31 May 2018 - Pointfuse, a software provider that converts laser scan data - known as point clouds - into 3D models, has announced its integration with Autodesk’s BIM 360 project delivery platform, the first single project platform connecting design through construction. Pointfuse has pioneered new techniques to rapidly process vast point cloud datasets into intelligent and easy to handle mesh models. This innovation will allow important ‘as built’ models to be easily incorporated into Building Information Modelling (BIM) workflows managed with Autodesk BIM 360.
“Digital Construction is about supplementing existing work flow practices in an online collaborative environment,” said Jon Boyce, CEO of Pointfuse. “Collaborative design is well established as a deliverable in digital construction. As a leading provider of robust design and construction software, Autodesk is the natural partner for us to introduce this exciting development in reality capture to achieve 4D BIM.”
“The integration of Pointfuse with BIM 360 bridges the gap between the physical and digital worlds, and allows a far more connected and efficient construction process,” says Sarah Hodges, Sr. Director, Construction Business Strategy and Marketing at Autodesk. “When reality capture data is exported to BIM 360, Pointfuse models allow easy comparison to the original design at each stage of the building process and puts all the project data into a single platform, breaking down silos that currently exist among various technologies.”
4D BIM is a way of live-tracking project progress so stakeholders can view the current status and collaborate online using the BIM 360 platform. The latest scheduling and updated costings, changes and identification of potential clashes all require timely and accurate “as built” data as an integral part of the process. “Crucially Pointfuse as built models will help with the avoidance of design clashes. This is important since, industry-wide, it is estimated that each identified clash could save up to $15,000 on a large scale project,” comments Boyce.
Democratization of data from job sites has been a process of box ticking and photography which is traditionally a manual and cumbersome process. The data compiled is considered to be of low value and rarely adopted in a complete work flow process. New rapid scanning technology offers the potential to change this, but the resulting point cloud is difficult to interpret by the untrained eye. Its use in a browser based collaborative environment is also compromised by the large data file sizes. Pointfuse addresses this with software that creates relatively small but accurate and intelligent models that are manageable within a cloud environment.
Pointfuse is available as a download from the Autodesk App store as a full integration with BIM 360.
CONTACTS:
Editorial enquiries to Robert Peel, robert@market-it.co.uk, +44 (0)1666 823306
Reader and advertising enquiries to Mark Senior, mark.senior@pointfuse.com, +44 (0)20 8017 8600
Color separation requests to robert@market-it.co.uk
Notes to Editors:
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. 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.
Offering ‘selectable surfaces’, Pointfuse V3 provides a unique approach. 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 also significantly reduces the file size of 3D models created from point clouds. In simple terms, the data density within each surface is reduced whilst still maintaining the fidelity of the model. This results in a reduction in model size by a factor of ten, making ongoing use of the model easier, faster and more efficient.