From visualising massive 3D point-cloud data through to real-time IoT asset monitoring to change detection of complex geotechnical faults, Euclideon is leading the digital mine revolution. Read on to see how Euclideon helps mining companies take advantage of digitisation to increase efficiency and productivity safely while minimising costs.
Huge data sets have been a challenge for the public works industry. 3D datasets have resulted in extremely long periods of post-processing, and often an end product that’s too large to be viewed in its entirety. Euclideon makes these problems a thing of the past, by introducing a platform that allows teams to view huge datasets without the needs for expensive work stations - saving the client money and time.
Euclideon is heavily involved in the Defence industry. Our Unlimited Detail™ is the core engine for both the udStream and udSDK software platforms, and drives holographic hardware and AR/VR platforms...
Road & Rail
The road and rail industries have been capturing LiDAR scans of their huge networks to help improve their ever growing infrastructure. The challenge the industry faces is the sheer size of their datasets, which tends to grow extremely quickly, and companies then struggle to manage so many individual point cloud files.
Euclideon's udStream platform allows our road and rail customers to view, manage and share all their individual files in one easy to access platform that easily scales as their data needs grow.
Underwater visualisation is our most recent growth area. Customers in the defence, government, transport or utilities industries have struggled capturing photogrammetry models in murky, poorly lit conditions, while visualising ‘point cloud’ radar and lidar scans of large subterranean landscapes has traditionally been expensive and inefficient.
Euclideon’s udStream platform not only provides rapid visualisation of large point cloud datasets in Unlimited DetailTM, but also allows customers to combine datasets from different sources into the same 3D model – bringing context to previously disparate datasets across vast geographies and timelines.