Leica Point Cloud

[Blanche Bunnell]

As the market leader in reality capture software, Leica Geosystems has pioneered products that enable users to capture, prepare, manage and analyse reality capture data efficiently and professionally. From the moment you begin capturing 3D data to 3D model creation, analysis, and collaboration with clients, Leica Geosystems software seamlessly empowers users at all workflow stages.

Prepare point cloud data in the field, conduct final registration, and publish with Leica Geosystems reality capture software. Leica Cyclone REGISTER 360 PLUS connects to your sensor and Cyclone FIELD 360 so you can stream data and take fast and full advantage of field work. Publish data to industry standard formats, our internal LGS format, or Leica Cyclone ENTERPRISE.

Leica Geosystems offers data management software to meet the needs of any project. LGS files store points, images, GeoTags, measurements, markups, models, and more and can be used in analysis and deliverable creation products. For enterprise-level teams, Leica Cyclone ENTERPRISE serves as a single source of truth deployed locally or in the cloud for worldwide collaboration.

With Leica Geosystems analysis software, data goes from points and images to functional deliverables that enable clients to make data-driven decisions. Produce your deliverables directly in a CAD package with Leica CloudWorx, use Leica Cyclone 3DR for all-in-one modelling and analysis, or work with an industry-specific package like Leica Map360 for forensic investigations.

Leica Geosystems offers a collection of exclusively developed software leveraging JetStream technology to enable smart, publish-once workflows. Powered by JetStream products support simple, integrated workflows, delivering instant visualisations and full data access for in-field applications, automated BIM extractions, mesh models, and more.

Reality Cloud Studio, powered by HxDR, is Hexagon's cloud application for reality capture data visualisation, collaboration, and storage. It is compatible with all Hexagon software and directly interfaces with Leica Geosystems hardware and software. Upload your own data into Reality Cloud Studio for automated meshing and registration, immersive visualisations and virtual tours, collaboration among stakeholders, and secure data storage.

I am trying to better understand the workflow for importing point clouds from Leica Cyclone (v. 5.1) to ArcGIS (v. 10.2.2). Within Cyclone, I understand that I can set the XYZ of a location below the lidar scanning unit. Following the acquisition of a number of scans, I can tie them all together. Once I have a merged point cloud, I will have a PTS/PTX file which I can then convert to LAS using this LAStools (Pointzip: Compressing Leica's Cyclone PTS and PTX files with LASzip) tool. However, it is unclear to me how I can project this data into a projected coordinate system like UTM Zone 17N. How is this possible? Is this done within ArcGIS or Cyclone?

In Cyclone when you are doing registration of scan (combining all scans), you may also add coordinate file for target in projected coordinates and set this file as home scan. this will make all data to UTM coordinates. than export to PTX/PTS etc.

Resource allocation centres on memory (RAM) usage and how the demand for RAM increases with the number of threads used in parallel. For any new job, the system looks at the format type, size and resolution of the dataset, then intelligently estimates the amount of RAM that will be used per thread and allocates the number of threads accordingly.

If too many threads were assigned to a registration during import, the workstation could quickly run out of memory, which could mean the software crashes hours into a job. As you might expect, Leica Geosystems has designed this process such that stability takes precedence, which means the software can be quite conservative in the way it allocates resources.

For our testing we focused on import / registration and the time it takes to complete the process. We used a variety of different workstations, but most of our testing centred on the Scan 3XS GWPCAD Q116C, which features an 8-core Intel Core-i9 9900K CPU.

As mentioned previously, the amount of system memory (RAM) dictates the number of threads that are allocated on import. With the Breakers dataset we found a workstation with 16GB used one thread, one with 32GB used two threads, one with 64GB used five and one with 128GB used six.

Using Windows Performance Monitor we tracked memory usage and found it changed significantly during import, forming peaks and troughs as each new setup started and finished. At no point did the system ever come close to running out of memory, leaving plenty of room for error, or capacity for the user to multitask and run other software on the same workstation.

With this in mind, Cyclone Register 360 is very well suited to overclocked workstations where the frequency of every CPU core is permanently boosted. With CPUs that run at standard clock speeds, only one or two cores go into Turbo.

Out of all the CPUs we tested for this article, the ten core Intel Core i9-10900K (Q2 2020) performed best. In the Scan 3XS GWP-ME Q120C workstation all cores were overclocked to 5.0GHz. This was closely followed by the eight core Intel Core i9-9900K, which was at a slight disadvantage as it ran at its stock 3.6GHz up to 5.0GHz Turbo in the Scan 3XS GWP-CAD Q116C workstation.

But choosing a CPU for point cloud registration is not just about getting the best perfomance in Cyclone Register 360. There is a potential benefit to having more than 8 or 10 cores in that the CPU will enable better multitasking. Not many people want to sit at their desks twiddling thumbs while waiting for the registration to finish. So investing in a machine with 12, 16 or 18 cores and using processor affinity in Windows to pin Cyclone Register 360 to 8 or 10 of those cores, would leave the remaining CPU cores free to work with other applications.

Workstations used to be Intel all the way, but in the last couple of years AMD has started to offer some serious competition. This is especially true with 3rd Gen AMD Ryzen (available with 8, 12 or 16 cores) and 3rd Gen AMD Ryzen Threadripper (available with 24, 32 or 64 cores).

With the 32-core 3rd Gen Threadripper 3970X we saw a small benefit when using all 32 cores, compared to 16 or 8 (with all other cores disabled). This is also testament to the excellent cooling in the Armari Magnetar X64T-G3 FWL which uses AMD Precision Boost Overdrive to full effect, allowing all cores to run at very high frequencies (see here for a full review).

While PCIe NVMe SSDs boast significantly higher sequential read / write speeds than SATA SSDs, this only really benefits workflows that use large continuous datasets. This is not the case for RAW point cloud data from the RTC360, nor the project data created by Cyclone Register 360.

Our 99GB dataset for example, comprises 7,400 files (0.2MB to 226MB in size) and the registered dataset 320 files (1MB to 700MB in size), with many other smaller files created in the temp folder along the way. And when it comes to reading and writing these types of files, which often happens concurrently throughout the import process, the SATA SSD appears to do an equally good job. More importantly perhaps, considering the amount of processing that is needed for registration, one can presume that storage is not really a bottleneck.

In addition, our tests show that it is possible to use a combination of SSD and HDD without negatively affecting performance. By putting all the RAW point cloud data on an HDD, and the main storage and archive folder on an NVMe SSD it had virtually no impact on import time (see chart 5 below).

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