Point clouds created by laser scans can store extremely detailed physical space information. However, the development of higher point cloud data can lead to larger files that can become unusable to store and process, especially as projects grow in size.

Hundreds or even thousands) of scans may be used in large projects, often generating datasets over a terabyte. This will place a strain on computer hardware, CPU, and RAM monitoring and significantly increase the time it takes to process data from laser scans.

For those working with point clouds, dealing with datasets of this scale is a growing challenge. Cloud data processing software like ScanX can manage unbelievably massive datasets and theoretically process an infinite number of tasks all at once. But it all depends on how “user-friendly” the software for point cloud processing is. The cloud can be the solution for processing the enormous datasets of point clouds at speed. Theoretically, limitless computing capacity is provided by the cloud. But in leveraging the power, there are also practicalities involved.

Scalability

The cloud will allow a surveyor to apply more powerful processing as needed quickly. Many cloud providers, for example, may expand existing resources to meet additional business needs or modifications. Without always having to purchase new hardware or upgrade your existing

IT infrastructure, you will be able to sustain your business growth.

To manage vast volumes of data, cloud services provide both computing memory (RAM) and disk storage. To deal with the inevitable rise in both the usage and scale of point clouds, terabytes of RAM and petabytes of storage may be made available.

Processing software must be capable of interacting with the cloud to take advantage of cloud computing. You will also need to invest in a high-quality internet connection to ensure no data transfer limit occurs.

When you transition to a cloud solution, the CPU will no longer constrain

your ability to process at speed and rely more on your capacity.

On-demand processing

Cloud platforms may deliver significant operational enhancements, with far more processor cores and higher output specs than a standard laptop or desktop. An additional advantage is that you can only pay for this pool of resources while using it.

In the cloud, you can dynamically scale your resources to suit the exact projects you are running at any given time rather than calculating what you will need and investing in the hardware. When you don’t need them, there is the opportunity to turn off services or ramp them up when you do.

The optimal scenario is to build a project pool of the correct size with virtually continuous use of cloud resources. Cloud resource cost control can be a science in itself, so consideration must be taken to ensure that cost and performance are in harmony. But there’s an opportunity to pay for only what you need, and there’s no need to tie up your computer for hours when you can instead use someone else’s.