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Updated: September 17, 2015

Shearspace is an online visualization and data analytics platform that leverages the global Amazon Cloud infrastructure to perform the heavy lifting of real-time rendering and computationally heavy tasks. Initially aimed at LiDAR data visualization (billions of points) and visual data sharing (keep your data private), Shearspace has prototypes ready to extend into automatic data processing of LiDAR data, generalised CAD manipulation and simulation mechanics.

Shearspace users need only have an updated Firefox, or Chrome, HTML browser to start using it (no GPU required; not a WebGL solution).  Due to there being no installed specialist software, users no longer need to purchase expensive graphics workstations to view extremely large datasets interactively.  A by-product of this model is that users will always have the latest, up to date, software deployment.

No longer requiring lock-in contracts that tie software to a specified number of users or machines, Shearspace utilizes a low cost, pay-by-the-hour model that enables users to scale their usage to what is needed, when they need it and to cost optimize their IT software (and hardware) expenditures by paying for only what they use. No payment for separate modules; all inclusive.

Image Teasers
LiDAR: Mobile Laser Scanning LiDAR: Interact with LiDAR colourmaps 2D LiDAR mapping 2D LiDAR mapping with zoom
Mobile Laser Scanning Interactive colormaps 2D Mode 2D Zoom/Pan
Cloud resources to simulate mechanics Create LiDAR movies in HTML Create large scale LiDAR visualizations
Environment simulation Full Data Manipulation UltraHD Movie Generation Large Scale 3D


As Shearspace becomes more stable and the content delivery engine stabilises, the next phase of development will push towards implementation of sophisticated algorithms to manipulate, interrogate and extract useful data from the point cloud. Efforts will mainly concentrate on these being automatic, although we understand that there will always be edge cases with point clouds that will require a small degree of manual processing.

Shearspace has been built with a scalable, parallel, computational engine that enables highly computational algorithms to be applied to the point cloud. Some of these algorithms require potentially 100's of CPU hours (1 cpu running for hundreds of hours or hundreds of cpus running for an hour) to deliver highly accurate results. Shearspace allows users to scale up their computational requirements simply, cheaply and on demand. This scaling can also take advantage of Amazon spot pricing which means that you can get 100 compute cores for less than a dollar.

Automatic Classification

The automatic point classification algorithm is coming along really well. For now, I've decided to concentrate on Mobile Laser Scanning data, although I periodically run it over the Aerial test suite with results that are suprisingly accurate.

At the moment, I'm concentrating on classifying GROUND, ROAD and NOISE (note, noise includes cars, etc on the road). Signage, guard rails, gantries and trees are mostly working but it is more that I'm having to build in the ability for people to add known 'objects' to be identified.

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The price per hour ( USD ) depends on which Amazon data center you select when launching a Shearspace session as AWS prices vary across regions. By default, Shearspace will automatically pick the best data center based on your physical location to get the best performance based on several factors. However, it is possible that you can tweak Shearspace settings such that you get sufficient performance from data centers across the world (for example, Shearspace HQ in Brisbane, Australia, can run content out of California, USA, easily but can experience lag at certain times of the day).


Please do not hesitate to contact us.

Shearspace engineers are a small, but dedicated group, who have a long history in supercomputing, peta-scale data management and scientific visualization in all areas of science, technology, engineering and mathematics. Shearspace is an attempt to bring the skills, technologies and analytics out of the academic space and into the business domain. Doing so simply and cost effectively.

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