I am now getting very close to a version 1.0 release of my software. It has taken quite a lot of effort to get it to this stage and has required me to solve quite a lot of very difficult issues along the way (you should see the performance of the new ray-box intersection algorithm I came up with – realtime graphics now available on a cpu). I’m currently filing global patent applications for quite a few things (on the advice of a few interesting people).
Shearspace provides a simple way to perform massive scale, real-time visualisation of datasets all within a HTML browser (no extensions, no WebGL, no GPU, purely native browser). It can deliver FullHD (1080p) resolution at 30 frames per second for total interaction of monster sized datasets. The nature of Shearspace also enables people to share their datasets with stakeholders but not compromise their data security. That is, if you own sensitive data, you can allow specific people to view that data without ever having to give them a physical copy. This helps on two main fronts: data security and the difficulties in moving very large datasets around. You can set a variety of rules regarding the extent stakeholders may view data.
Shearspace loads data files natively as it does not require specialised data formats. You can thank years of HPC performance programming for that one. Data files can be stored in AWS S3 at the standard pricing model (typically 3 cents per GB per month). As a result, you are not limited to just visualising a single data type; You can view RGB, Intensity, point returns, classification, scan angles, height; The full range of data. These data types can be manipulated with a user defined 1 dimensional transfer function for interactive colour mapping.
The renderer itself is capable of drawing in excess of 12 billion points per second (single core). This performance will scale proportionally as modern cpu architectures progress as well as being multi-core (but I only use one core for the rendering at this point because that is all I need). Of course, given the remote visualisation nature of the software and that the rendering resources can be located thousands of kilometres away, rendering speed is the least of the problems. I think that the lag, bandwidth and other network problems have been solved nicely. Higher geometric primitives are also ready but not flagged for version 1.0 at this stage.
People will no longer have to purchase expensive graphics workstations and deal with expensive, and restrictive, software licenses that require yearly maintenance. Shearspace is available on demand with a very cheap pay-by-the-hour model. This enables clients to cost optimise their IT infrastructure.
I’m not sure yet on the absolute release date into the global AWS cloud as I’m still stress testing the performance of encryption and machine scaling (I have to ensure that my load balancer/monitor can handle up to at least 10,000 AWS instances, globally located, seamlessly).
If anybody would like to have a look and play with a pre-release version, just email me at the contact details at the bottom of the Shearspace website. I’d like to thank QCIF and QRIScloud for providing me with resources to enable client testing. If Shearspace is in anyway useful, I hope to forge a deal to deliver Shearspace at almost cost price using QCIF resources for Australian consumers. The Shearspace catch being that people take the time to report bugs and provide feedback.
I’ve had a lot of fun writing this software. It is really nice to devote fifteen months of effort into an idea of remote visualisation that I’ve been mentally cooking up since 2003 (my first IEEE remote visualisation paper, on mobile devices before smart phones existed!) and utilises everything I’ve learnt during my education and working career (20 years in total).
I’ll get some videos up online as soon as I can figure out a cost effective way of hosting them that does not compromise video quality (grrrr … youtube and vimeo … why not a high quality service???!!!!). Haven’t even a clue regarding hosting of the UltraHD videos (which can be created using Shearspace via its online video creator/editor).
Flagged for version 2.0 release is the point cloud classification algorithm. Shearspace has been built with a scalable, parallel, computational engine that enables highly computational algorithms to be applied to the point cloud. This is something that vendors cannot do with even powerful workstations. These algorithms require potentially 100’s of compute hours to deliver accurate results. Shearspace allows users to scale up their computational requirements simply, cheaply and on demand. I hope to also develop the ability to extend into Amazon spot pricing (100 compute core hours for less than a dollar). But my classification engine will take next priority.
Hopefully by the time version 3.0 rolls around, we’ll have some money to hire UX/UI developers to start building the manual measuring, tagging, cleaning and generalised CAD tools for both 2D and 3D views.