Cintoo is adding new portfolio options to its Cintoo platform – the BIM and Twin Editions.

The Cintoo platform, which is focused on reality data, allows users to upload and stream huge 3D data files from any desktop or laptop via a web browser. Users can compare reality data to their BIM and CAD models or scans to scans for project collaboration and optimisation.

The new BIM Edition is designed for AEC-centric workflows and includes features such as progress monitoring and issue tracking for performing analysis and to help eliminate risk.

The Twin Edition of the Cintoo platform is aimed at huge industrial and manufacturing sites to help improve asset visibility, digital twin management, and optimise facility maintenance and rework.

Cintoo has also introduced a new look and feel to the Cintoo platform. According to the company, it’s now easier to access key tools and the updated head ribbon maximises project space, as panels have been moved to the menu on the right-hand side.

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Precise reality capture has come a long way. We are in the process of moving from rare and expensive to cheap and ubiquitous. Laser scanning manufacturers are currently holding their price points and margins, but technology and mobility are closing in from the consumer end of the market. Matterport recently launched a low-cost laser scanner combined with photogrammetry, and Polycam, a developer of smartphone-based reality capture software for consumers, is looking to sell up to the professional market.

Polycam can be used to quickly document existing conditions (as-builts), measure spaces, and generate floor plans. The latest release looks to dig deeper into AEC workflows. The app is available for iOS and Android and makes use of the iPhone’s built in LiDAR and cameras to capture interiors and exteriors when using footage from a drone. The software also supports Gaussian Splats to achieve high-resolution 3D capture. While the product has proved incredibly popular, the firm is looking to move into new areas of AEC, such as interior design, structural, construction inspection and facilities management.

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The company

Polycam was founded four years ago by Chris Hinrich and Elliot Spellman. Their initial aim was to build software that could deliver the power of 3D capture to users of smartphones.

Before Polycam, the pair worked at a company which was developing a ‘3D Instagram’ that processed uploaded images on a server for photogrammetry. This was a bottleneck. The pair left the company and set up Polycam. The big innovation was the fact that you could process the 3D creation fast, on device.

With over half the Fortune 500 companies actively using Polycam and well over 100,000 paying users, the firm has been able to raise over $22 million in 2024 in investment, based on revenues of $6.5 million in 2023. One of the core areas that showed regular growth was in their AEC user-base. The latest release focuses on providing tools for the growing base of AEC customers.

New features

Polycam supports Apple’s AR toolkit, allowing for easier and more accurate model creation by recognising walls, doors, and windows. I have used Polycam on my iPhone and compared it to a Leica Disto and have found the accuracy to be within a few millimetres when scanning a room. This makes it suitable for schematic designs and perhaps material ordering (though precise cuts might still require manual measurements). The platform supports multifloor scanning, to build a model very similar to that of Matterport.

While an automated scan-to-BIM workflow is seen as the aim, Polycam offers a service where users can order professional-grade 3D files that are then converted into CAD (AutoCAD) and BIM (Revit) files – but with a human-in-the-loop, through a collaboration with Transform Engine. This provides a higher quality and more detailed BIM output than automatic processing currently offers. AutoCAD layouts start at $95 and Revit models $200. Furthermore, Polycam has plans to add IFC (Industry Foundation Classes) file export, which will make it easier for users to create their own models.

That said, Polycam does instantly generate customisable 2D floor plans from its scans. These floor plans can be tweaked within the app for business and enterprise tiers, allowing for adjustments to wall thickness, colours, and labels.

Complex geometry can fool the application. I found that accurately capturing ceilings with multiple levels and stairs, resulted in gaps in the models

There’s a new AI Property Report, which automatically generates PDFs and includes the floor plan along with information such as the number of bedrooms and bathrooms, floor area, total wall area, and a room-by-room breakdown with measurements. This could be used for insurance or costing and ordering materials. The AI automatically derives room classifications by detecting objects like beds (for bedrooms) and appliances (for kitchens).

The new Scene Editor allows multiple scans to be combined, including both interior captures and drone footage, into a single, unified 3D scene. This provides a holistic view of a property or project site, enabling users to navigate and analyse the entire space. Using layers, it’s possible to filter scenes and control the visibility of different parts of a capture.

The platform also has new collaboration and sync tools that allow users to add comments and start threaded conversations within a scanned space, facilitating review processes for architects and other stakeholders. The cross-platform nature of Polycam ensures that teams can access and share this data across various remote devices.

3D Generator

The latest version offers a quick way of making 3D components for a library, from real world objects like a chair, starting from an image or a prompt, describing the details of the object you would like to create. This isn’t just the geometry, but the materials used too. These 3D objects can be placed in the real-world scans, enabling users to visualise and design spaces with custom virtual objects.

Limitations

Because everything is on device and there is no option for cloud or serverbased processing, there comes a natural limit. On-device memory is also a constraint. Polycam recommends a horizontal size limit of around 279 sq metres for a single scan, to ensure a decent result. Beyond this, the app might require compromises to process quickly without running out of memory. While the new scene editor addresses combining multiple scans, individual scans still have practical size limits.

Complex geometry can fool the application. I found that accurately capturing ceilings with multiple levels and stairs, resulted in gaps in the models. While the technology has improved, complex or non-planar geometry in older buildings might still present some challenges.

While Polycam is accurate enough for schematic designs and potentially ordering bulk materials (the company claims within 2% compared to expensive LiDAR scanners), it might not be sufficient for tasks requiring very high precision, such as cutting kitchen cabinets, which may still necessitate manual measurements. Also while using the AR Toolkit object recognition the spatial reports is not totally foolproof and may require users to manually override classifications if they are incorrect.

Polycam seems to have approached the market more aimed at construction and its use in the American market. While this is predominantly 2D, the BIM side of the product has a lot yet to be delivered connecting the data on device to BIM software. Scan-to-BIM still requires the cost and eye of a human to properly check the conversion. This has to be compared to having a professional survey and the legal indemnity that it provides. Would I use Polycam on a house? Hell yes! Would I use it on a major airport refurbishment? Only as a quick rough.

Conclusion

Polycam is certainly on the right path with its concentration of development of instant 2D floor plan generation and measurements, as well as building 3D models for AEC users. AR Toolkit’s intelligence always seems like magic when scanning a room. However, the software and service does have limitations with obvious omissions and the need for closer integration with AEC workflows. Surely, we can’t be too far away from not requiring a human in the loop to create reliable results from scan to BIM?

Size matters. While the possibility of real-time streaming of large-scale scans is a compelling idea for future development, the current focus of Polycam appears to be on enhancing on-device processing and providing relevance to the AEC industry. The planned addition of features like IFC export and improved BIM workflows indicates a clear direction towards serving the professional needs of architects, engineers, and construction professionals.

Despite these limitations, the monthly use cost is $17 per user (Pro) and $34 per user (Business level). At those prices, it’s an application that many in the industry might well use regularly, when onsite vs the alternative. This is like having a budget Matterport scanner in your pocket.

The ongoing development and the specific features being introduced demonstrate a clear trajectory towards making Polycam a better fit for AEC professionals, especially surveyors and architects, particularly for initial site assessment, as-built documentation, schematic design, and collaboration.

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Topcon and Faro have announced a strategic agreement to ‘develop and distribute innovative solutions’ in the laser scanning market. The companies expect the agreement to expand access to digital reality solutions and result in complementary product developments, such as the seamless integration of Topcon and Sokkia solutions with Faro’s solutions.

The collaboration will focus on harnessing the companies’ collective expertise in laser scanning technologies, targeting key sectors including, construction, surveying, mapping, architecture, forensics, BIM, and industrial plant and process applications.

“With this agreement, we are confident that the solutions we provide will be further enhanced and contribute to overcoming the challenges our customers face,” said Tetsuya Morita, senior executive officer, general manager, smart infrastructure business division, Topcon Corporation. “By leveraging the expertise and technological capabilities of both companies, we will offer more comprehensive reality capture solutions.”

“We believe our collaboration with Topcon is an exciting step in making Faro’s state-of-the-art reality capture solutions more widely accessible,” said Peter J. Lau, Faro president and CEO.

“With Topcon’s established distribution channels and expertise in delivering geospatial solutions, professionals around the world will have access to the best tools to enhance productivity, accuracy, and efficiency in their industries.”

Through this agreement, Faro and Topcon plan to introduce initiatives to further the collaboration, including product offerings and enhanced software integrations.

Image caption: pictured from left: Ewout Korpershoek and Tetsuya Morita with Topcon, Matthew Horwath and Peter J. Lau with Faro, Ivan Di Federico with Topcon, Phillip Delnick with Faro, and Murray Lodge and Luc Le Maire with Topcon.

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Reality modelling is one of the most computationally demanding workflows in Architecture, Engineering and Construction (AEC). It involves the creation of digital models of physical assets by processing vast quantities of captured real-world data using technologies including laser scanning, photogrammetry and simultaneous localisation and mapping (SLAM).

Reality modelling has numerous applications, including providing context for new buildings or infrastructure, forming the basis for retrofit projects, or comparing “as-built” with “as-designed” for construction verification.

This article is part of AEC Magazine’s 2025 Workstation Special report

While there’s a growing trend to process captured data in the cloud, desktop processing remains the preferred method. Cloud can be costly, and uploading vast amounts of data — sometimes terabytes — is a significant challenge, especially when working from remote construction sites with poor connectivity.

Processing reality capture data can take hours, making it essential to select the right workstation hardware. In this article, we explore the best processor, memory and GPU options for reality modelling, testing a variety of workflows in three of the most popular tools — Leica Cyclone 3DR, Leica Cyclone Register 360, and RealityCapture by Capturing Reality, a subsidiary of Epic Games.

Most AEC firms have tight hardware budgets and it’s easy to spend money in the wrong places, sometimes for very little gain. In some cases, investing in more expensive equipment can even slow you down!

Leica Cyclone 3DR

Leica Cyclone 3DR is a multi-purpose reality modelling tool, used for inspection, modelling and meshing. Processing is done predominantly on the CPU and several tasks can take advantage of multiple CPU cores. Some tasks, including the use of machine learning for point cloud classification, are also optimised for GPU.

For testing we focused on four workflows: scan-to-mesh, analysis, AI classification and conversion.

Scan-to-mesh: Compared to point clouds, textured mesh models are much easier to understand and easier to share, not least because the files are much smaller.

In our ‘scan-to-mesh’ test, we record the time it takes to convert a dataset of a building — captured with a Leica BLK 360 scanner — into a photorealistic mesh model. The dataset comprises a point cloud with 129 million points and accompanying images.

The process is multi-threaded but, as with many reality capture workflows, more CPU cores does not necessarily mean faster results. Other critical factors that affect processing time include the amount of CPU cache (a high-speed onchip memory for frequently accessed data), memory speed, and AMD Simultaneous Multithreading (SMT), a technology similar to Intel Hyper-Threading that enables a single physical core to execute multiple threads simultaneously. During testing, system memory usage peaked at 25 GB, which meant all test machines had plenty of capacity.

The most unexpected outcome was the 8-core AMD Ryzen 7 9800X3D outperforming all its competitors. It not only beat the 16-core AMD Ryzen 9 9950X and Intel Core Ultra 9 285K (8 performance cores and 16 efficient cores), but the multicore behemoths as well. With the 96- core AMD Threadripper Pro 7995WX it appears to be a classic case of “too many cooks [cores] spoil the broth”!

The AMD Ryzen 7 9800X3D is a specialised consumer CPU, widely considered to be the fastest processor for 3D gaming thanks to its advanced 3D V-Cache technology. It boasts 96 MB of L3 cache, significantly more than comparative processors. This allows the CPU to access frequently-used data quicker, rather than having to pull it from slower system memory (RAM).

But we expect that having lots of fast cache is not the only reason why the AMD Ryzen 7 9800X3D comes out top in our scan-to-mesh test – after all, Threadripper Pro is also well loaded, with the top-end 7995WX having 384 MB of L3 cache which is spread across its 96 cores. To achieve a high number of cores, modern processors are made up of multiple chiplets or CCDs. In the world of AMD, each CCD typically has 8 cores, so a 16- core processor has two CCDs, a 32-core processor has four CCDs, and so on.

Communication between cores in different CCDs is inherently slower than cores within the same CCD, and since the AMD Ryzen 7 9800X3D is made up of a single CCD that has access to all that L3 cache, we expect this gives it an additional advantage. It will be interesting to see how the recently announced 12-core Ryzen 9 9900X3D and 16-core Ryzen 9 9950X3D compare. Both processors feature 128 MB of L3 cache and comprise two CCDs.

Simultaneous Multithreading (SMT) also has an impact on performance. With the AMD Ryzen 9 9950X, for example, disabling SMT in the BIOS cut processing time by as much as 15%. However, it had the opposite effect with the AMD Ryzen 7 9800X3D, increasing processing time by 32%.

Memory speed also has an impact on performance. The AMD Ryzen 9 9950X processor was around 7% slower when configured with 128 GB RAM running at 3,400 MT/sec than it was with 64 GB RAM running at the significantly faster 5,600 MT/sec.

Analysis: In our analysis test we compare a point cloud to a BIM model, recording the time it takes to calculate a colour map that shows the deviations between the two datasets. During testing, system memory usage peaked at 19 GB.

The process is multi-threaded, but certain stages only use a few cores. As with scan-to-mesh, more CPU cores does not necessarily mean faster results, and CPU cache, SMT and memory speed also play an important role. Again, the AMD Ryzen 7 9800X3D bagged first spot, completing the test 16% faster than its closest rival, the Intel Core Ultra 9 285K.

The big shock came from the 16-core AMD Ryzen 9 9950X, which took more than twice as long as the 8-core AMD Ryzen 7 9800X3D to complete the test. The bottleneck here is SMT, as disabling it in the BIOS, so each of the 16 cores only performs one task at a time, slashed the test time from 91 secs to 56 secs.

Getting good performance out of the Threadripper Pro processors required even more tuning. Disabling SMT on its own had a minimal impact, and it was only when the Cyclone 3DR executable was pinned to a single CCD (8 cores, 16 threads) that times came down. But this level of optimisation is probably not practical, not least because all workflows and datasets are different.

AI classification: Leica Cyclone 3DR features an AI-based auto-classification algorithm designed to ‘intelligently classify’ point cloud data. The machine learning model has been trained on large amounts of terrestrial scan data and comes with several predefined models for classification.

Two of our test workflows rely on Nvidia GPUs, but because they share some of the workload with the CPU, the performance gains from more powerful GPUs are less pronounced compared to entirely GPU-driven tasks like ray trace rendering

The process is built around Nvidia CUDA and therefore requires an Nvidia GPU. However, the CPU is still used heavily throughout the process. We tested a variety of Nvidia RTX professional GPUs using an AMD Ryzen 9 9950X-based workstation with 64 GB of DDR5 memory.

The test records the time it takes to classify a point cloud of a building with 129 million points using the Indoor Construction Site 1.3 machine learning model. During testing, system memory usage peaked at 37 GB and GPU memory usage at a moderate 3 GB.

The big takeaway from our tests is that the CPU does the lion’s share of the processing. The Nvidia RTX GPU is essential, but only contributes modestly to the overall time. Indeed, there was very little difference between most of the Nvidia RTX GPUs and even the entry-level Nvidia RTX A1000 was only 22% slower than the significantly more powerful Nvidia RTX 4500 Ada.

Conversion: This simple test converts a Leica LGSx file into native Cyclone 3DR. The dataset comprises a point cloud of a highway alignment with 594 million points. During testing, system memory usage peaked at 11 GB. As this process is largely single threaded it’s all about single core CPU performance. Here, the Intel Core Ultra 9 285K takes first place, closely followed by the AMD Ryzen 9 9950X in second. With a slightly slower peak frequency the AMD Ryzen 7 9800X3D comes in third. In this case, the larger L3 cache appear to offer no benefit.

The Threadripper Pro 7975WX and Threadripper Pro 7995WX lag behind — not only because they have a lower frequency, but are based on AMD’s older ‘Zen 4’ architecture, so have a lower Instructions Per Clock (IPC).

Leica Cyclone Register 360

Leica Cyclone Register 360 is specifically designed for point cloud registration, the process of aligning and merging multiple point clouds into a single, unified coordinate system.

For testing, we used a 99 GB dataset of the Italian Renaissance-style ‘Breakers’ mansion in Newport, Rhode Island. It includes a total of 39 setups from a Leica RTC360 scanner, around 500 million points and 5K panos. We recorded the time it takes to import and register the data.

The process is multi-threaded, but to ensure stability the software allocates a specific number of threads depending on how much system memory is available. In 64 GB systems, the software allocates five threads while for 96 GB+ systems it’s six.

The Intel Core Ultra 9 285K processor led by some margin, followed by the 16- core AMD Ryzen 9 9950X and 96-core Threadripper Pro 7995WX. Interestingly, this was the one test where the 8-core AMD Ryzen 7 9800X3D was not one of the best performers. However, as the GPU does a small amount of processing, and Leica Cyclone Register 360 has a preference for Nvidia GPUs, this could be attributed to the workstation having the entry-level AMD Radeon Pro W7500 GPU.

Notably, memory speed appears to play a crucial role in performance. The AMD Ryzen 9 9950X, configured with 128 GB of 3,400 MT/sec memory, was able to utilise six threads for the process, but was 20% slower than when configured with 64 GB of faster 5,600 MT/sec memory, which only allocated five threads.

RealityCapture from Epic Games

RealityCapture, developed by Capturing Reality — a subsidiary of Epic Games — is an advanced photogrammetry software designed to create 3D models from photographs and laser scans. Most tasks are accelerated by the CPU, but there are certain workflows that also rely on GPU computation.

Image alignment in RealityCapture refers to the process of analysing and arranging a set of photographs or scans in a 3D space, based on their spatial relationships. This step is foundational in photogrammetry workflows, as it determines the relative positions and orientations of the cameras or devices that captured the input data.

We tested with two datasets scanned by R-E-A-L.iT, Leo Films, Drone Services Canada Inc, both available from the RealityCapture website.

The Habitat 67 Hillside Unreal Engine sample project features 3,199 images totalling 40 GB, 1,242 terrestrial laser scans totalling 90 GB, and uses up 60 GB of system memory during testing. The Habitat 67 Sample, a subset of the larger dataset, features 458 images totalling 3.5 GB, 72 terrestrial laser scans totalling 3.35 GB, and uses up 13 GB of system memory.

The 32-core Threadripper Pro 7975WX took top spot in the large dataset test, with the AMD Ryzen 9 9950X, AMD Ryzen 7 9800X3D and 96-core AMD Threadripper Pro 7995WX not that far behind. Again, SMT needed to be disabled in the higher core count CPUs to get the best results.

Memory speed appears to have a huge impact on performance. The AMD Ryzen 9 9950X processor was around 40% slower when configured with 128 GB of RAM running at 3,400 MT/sec than it was with 64 GB running at the significantly faster 5,600 MT/sec.

Import laser scan: This process imports a collection of E57 format laser scan data and converts it into a RealityCapture point cloud with the .lsp file extension. Our test used up 13 GB of system memory.

Since this process relies heavily on single-threaded performance, single-core speed is what matters most. The Intel Core Ultra 9 285K comes out on top, followed closely by the AMD Ryzen 9 9950X. With a slightly lower peak frequency, the AMD Ryzen 7 9800X3D takes third place. The Threadripper Pro 7975WX and 7995WX fall behind, not just due to lower clock speeds but also because they’re built on AMD’s older Zen 4 architecture, which has a lower Instructions Per Clock (IPC).

Reconstruction is a very compute intensive process that involves the creation of a watertight mesh. It uses a combination of CPU and Nvidia GPU, although there’s also a ‘preview mode’ which is CPU only.

For our testing, we used the Habitat 67 Sample dataset at ‘Normal’ level of detail. It used 46 GB of system memory and 2 GB of GPU memory.

With a variety of workstations with different processors and GPUs, it’s hard to pin down exactly which processor is best for this workflow — although the 96-core Threadripper Pro 7995WX workstation with Nvidia RTX 6000 Ada GPU came out top. To provide more clarity on GPUs, we tested a variety of add-in boards in the same AMD Ryzen 9 9950X workstation. There was relatively good performance scaling across the mainstream Nvidia RTX range.

Thoughts on processors / memory

The combination of AMD’s ‘Zen 5’ architecture, fast DDR5 memory, a single chiplet design, and lots of 3D V-Cache, looks to make the AMD Ryzen 7 9800X3D processor a very interesting option for a range of reality modelling workflows — especially for those on a budget. The AMD Ryzen 7 9800X3D becomes even more interesting when you consider that it’s widely regarded to be for gamers. The chip is not offered by any of the major workstation OEMs — only specialist system builders like Armari.

However, before you rush out and part with your hard-earned cash, it is important to understand a few things.

1) The AMD Ryzen 7 9800X3D processor currently has a practical maximum capacity of 96 GB, if you want fast 5,600 MT/sec memory. This is an important consideration if you work with large datasets. If you run out of memory, the processor will have to swap data out to the SSD, which will likely slow things down considerably.

The AMD Ryzen 9 9800X3D can support up to 192 GB of system memory, but it will need to run at a significantly slower speed (3,600 MT/sec). And as our tests have shown, slower memory can have a big impact on performance.

2) AMD recently announced two additional ‘Zen 5’ 3D V-Cache processors. It will be interesting to see how they compare. The 12-core Ryzen 9 9900X3D and 16-core Ryzen 9 9950X3D both have slightly more L3 cache (128 MB) than the 8-core Ryzen 7 9800X3D (96 MB). However, they are made up of two separate chiplets (CCDs), so communication between the cores in different CCDs could slow things down.

3) Most of the reality models we used for testing are not that big, with the exception of the Habitat 67 dataset, which we used to test certain aspects of RealityCapture. Larger datasets require more memory. For example, reconstructing the full Habitat 67 RealityCapture dataset on the 96-core Threadripper Pro 7995WX workstation used 228 GB of system memory at peak, out of the 256 GB in the machine – and took more than half a day to process. Workstations with less system memory will likely have to push some of the data into temporary swap space on the SSD. Admittedly, as modern PCIe NVMe SSDs offer very fast read-write performance, this is not necessarily the colossal bottleneck it used to be when you had to swap out data to mechanical Hard Disk Drives (HDDs).

4) Multi-tasking is often important for reality modelling, as the processing of data often involves several different stages from several different sources. At any given point you may need to perform multiple operations at the same time, which can put a massive strain on the workstation. As the AMD Ryzen 7 9800X3D processor has only 8-cores and is effectively limited to 96 GB of fast system memory, if you throw more than one task at the machine at a time things will likely slow down considerably. Meanwhile Threadripper Pro is much more scalable as there are processors with 12- to 96-cores, and the platform supports up to 2 TB of DDR5-5200 ECC memory.

For a crude multi-tasking test, we performed two operations in parallel — alignment in RealityCapture and meshing in Leica Cyclone 3DR. The Threadripper Pro 7995WX workstation completed both tests in 200 secs, while AMD Ryzen 7 9800X3D came in second in 238 secs. We expect this lead would grow with larger datasets or more concurrent processing tasks.

In summary, your choice of processor will depend greatly on the size of datasets you work with, and the complexity of your workflows. For lighter tasks, the AMD Ryzen 7 9800X3D looks to be an excellent budget choice, but for more complex projects, especially those that require multi-tasking, Threadripper Pro should deliver a much more flexible and performant platform. Of course, you still need to choose between the different models, which vary in price considerably and, as we have found in some of our tests, fewer cores is sometimes better.

Thoughts on GPUs

Two of our tests — Reconstruction in RealityCapture and AI classification in Leica Cyclone 3DR — rely on Nvidia GPUs. However, because these processes share some of the workload with the CPU, the performance gains from more powerful GPUs are less pronounced compared to entirely GPU-driven tasks like ray trace rendering.

There’s a significant price gap between the Nvidia RTX A1000 (£320) and the Nvidia RTX 6000 Ada Generation (£6,200). For reconstruction in RealityCapture, investing in the higher-end model is probably easier to justify, as our tests showed computation times could be cut in two. However, for AI classification in Leica Cyclone 3DR, the performance gains are much smaller, and there seem to be diminishing returns beyond the Nvidia RTX 2000 Ada Generation.

While larger datasets may deliver more substantial benefits, GPU memory — a key advantage of the higher-end cards — appears to be less crucial.

Workstation technology on test

Below is a list of kit we used for testing. All machines were Windows 11 Pro 26100.

Armari Magnetar workstation with AMD Ryzen 7 9800X3D CPU (8 cores), 96 GB DDR5 5,600 MT/s memory and AMD Radeon Pro W7500 GPU (read our review).

Scan 3XS workstation with AMD Ryzen 9 9950X CPU (16 cores), 64 GB DDR5 5,600 MT/s memory or 128 GB DDR5 3,600 MT/s memory and Nvidia RTX 4500 Ada Generation GPU (read our review).

Scan 3XS workstation with Intel Core Ultra 9 285K CPU (8 P-cores and 16 E-cores), 64 GB DDR5 5,600 MT/s memory and Nvidia RTX 2000 Ada Generation GPU (read our review).

HP Z6 G5A workstation with AMD Threadripper Pro 7975WX CPU (32 cores), 128 GB DDR5 5,200 MT/s memory and Nvidia RTX A6000 GPU (read our review).

Comino Grando workstation with overclocked AMD Threadripper Pro 7995WX CPU (96 cores), 256 GB DDR5 4,800 MT/s memory and Nvidia RTX 6000 Ada Generation GPU. (read our review).

We also tested a range of GPUs, including the Nvidia RTX A1000 (8 GB), RTX A4000 (16 GB), RTX 2000 Ada (16 GB), RTX 4000 Ada (20 GB), RTX 4500 Ada (24 GB) and RTX 6000 Ada (48 GB).

Main image: Reality modelling data comes from multiple sources: the Leica BLK ARC autonomous laser scanning module riding steady on the Boston Dynamics Spot robot

This article is part of AEC Magazine’s 2025 Workstation Special report

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• Features
  • AMD Ryzen AI Max Pro: the integrated GPU comes of age
  • AMD Ryzen 9000 vs Intel Core Ultra 200S
  • Workstations for arch viz
  • The AI enigma – challenges for workstations
  • Nvidia RTX GPUs for Stable Diffusion
  • Z by HP Boost: GPUs on demand
  • Workstations for reality modelling

  Reviews

  • HP ZBook Firefly 14 G11 A mobile workstation
  • Lenovo ThinkPad P14s (AMD) mobile workstation
  • Scan 3XS GWP-A1-C24 and GWP-A1-R32 desktop workstations
  • Boxx Apexx A3 desktop workstation
  • Armari Magnetar MM16R9 desktop workstation
  • Comino Grando workstation RM

  News

  • Reshuffle spells end for Dell Precision workstation brand
  • Lenovo powers IMSCAD workstation as a service (WaaS)
  • Nvidia unveils ‘Blackwell’ RTX GPUs
  • HP to launch 18-inch mobile workstation
  • Nvidia reveals AI workstation
  • HP bets big on AMD Ryzen AI Max PRO processor
  • Intel Core Ultra 200HX and 200H processors launch
  • AMD Ryzen AI Max PRO ‘Strix Halo’ processor launches

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Topcon Positioning Systems and Pix4D have announced a strategic agreement that combines their expertise in geopositioning and photogrammetry solutions.

The collaboration includes Topcon becoming an authorized distributor of Pix4D’s photogrammetry software. According to Topcon, this will streamline the procurement process for end-users while ensuring comprehensive technical support.

“The integration of Topcon’s precision positioning technology with Pix4D’s photogrammetry expertise is another great example of the type of collaboration the geospatial industry has always thrived on,” said Murray Lodge, executive vice president of Topcon Positioning Systems. “This will provide professionals with seamless access to industry-leading solutions that combine our complementary technologies.”

“The agreement on close collaboration with Topcon marks an important milestone in Pix4D’s growth strategy,” said Andrey Kleymenov, CEO at Pix4D. “A combination of precision positioning technology from Topcon and advanced photogrammetry and GeoFusion algorithms from Pix4D creates a powerful set of solutions for professionals in the utilities, infrastructure, and horizontal construction markets globally.”

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Written by Aaron Perry, Head of Digital Design at Allford Hall Monaghan Morris, and Andy Watts, director of design technology at Grimshaw

This must read report details what the AEC industry wants from future design tools, covering everything from data framework, context and scale, responsible design, and modular construction, to user experience, modelling capabilities, automation, intelligence, deliverables and more.

Watch the NXT DEV presentations from Aaron Perry and Andy Watts

NXT DEV 2023 – watch the video on NXTAEC.com

Aaron Perry, talking on behalf of a collective of medium-to-large AEC firms, gives a masterful presentation as he introduces the ‘Future Design Software Specification’.

NXT DEV 2024 – watch the video on NXTAEC.com

Andy Watts gives an important update on the specification, then hands over to Allister Lewis, ADDD, to talk about benchmarking software against the specification.

The post Future of AEC Software: Special Report appeared first on AEC Magazine.

dConstruct Robotics, the Singapore-based developer of SLAM-based mobile mapping solutions including a backpack and wheeled robot, has announced that its d.ASH Xplorer platform is now integrated with Autodesk Construction Cloud.

Project teams can import 3D models from Autodesk Build, Autodesk Docs, or BIM 360 into d.ASH Xplorer to compare 3D point cloud scans with BIM data managed within Autodesk Construction Cloud, offering new capabilities such as Scan2BIM, Scan2Twin, and Scan2Segment.

Scan2BIM is designed to precisely detect changes, ensuring projects remain aligned with design specifications.

Scan2Segment identifies particular objects and geospatial data at scale within a point cloud.

Scan2Twin creates a 3D neural reconstruction of the built environment from point clouds. According to the developers, it enables precise metric measurements and offers a deeper understanding of spatial dynamics within the project together with photorealistic visualisation of the scene.

“Construction firms can minimise rework and avoid scheduling delays by quickly identifying deviations from initial plans,” said James Cook, director of industry & technology partnerships at Autodesk. “By integrating 3D models from Autodesk Construction Cloud with d.ASH Xplorer, builders are able to detect and address issues early before they seriously impact the project budget and timeline.”

d.ASH Pack is an end-to-end 3D mapping solution that uses SLAM technology. It can be mounted on a backpack or on d.ASH-ER, a purpose-built four wheeled robot that performs multiple functions including reality capture.

Main image caption: d.ASH-ER pictured on stage with Nicolas Mangon, VP, AEC Industry Strategy at Autodesk, during the Autodesk University 2024 AECO keynote.

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Laser scanners were first commercially available in the mid 1990s and they have been used across nearly all AEC disciplines: civil engineering, surveying, architecture, heritage and quality control. The technology has moved from tripods to backpacks, handheld to mounted on robot dogs. However, the one thing that has remained stubbornly fixed is the high cost.

In 2012, Brian Mathews who previously ran Autodesk Labs, was made Autodesk VP of Reality Capture, as Autodesk anticipated the ‘democratisation of reality capture’. The closest that revolution came to being was in 2016 when Leica and Autodesk jointly launched the ultra-portable Leica BLK360 coupled with Autodesk Recap scanning software on the iPad.

At $15k the Leica BLK360 was half the price of most other scanners and looked set to be the breakthrough product that would bring prices down. Faro soon followed with a highly portable but more accurate scanner at around $20k but since then the appetite to drive costs lower has evaporated. If anything, most new scanning products have returned to previous price points and the Leica BLK360 will now set you back around $20k. In 2016, Mathews told AEC Magazine that to really open-up the market, scanners would need to drop below $10k. That moment never really materialised, until now.

Matterport was established in 2011 and brought out its first bespoke camera in 2012. Over time the technology improved to deliver 3D environments of interior scans, combining HDR photogrammetry with structured light sensors for depth information.

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Structured light sensors use a projector and cameras to capture an object’s 3D shape by analysing the deformation of a projected infrared light pattern. The most well-known application of this technology was from Microsoft with the Xbox One Kinect Sensor, an add-on for its gaming console.

Structured light sensor technology is significantly different to LiDAR, which uses time of flight of laser light to establish a cloud of 3D points.

Matterport became very popular in high-end real estate / estate agents and the company currently has 38 billion square feet of digital property currently under management in its platform.

Matterport technology has been used in architecture and construction to some degree, but not as a replacement for surveying.

The company shifted its business model to a subscription-based approach, added support for 360 cameras, and appeared to focus primarily on retail and real estate sectors.

The subscription pivot really only made sense to repeat volume users and not for occasional use. AEC is very much project focussed and that seemed to be a mismatch.

The last two years have seen two significant changes at Matterport.

The first was the release of the sub $6k Matterport Pro3, a LiDAR scanner and camera, which can cover 10-100m distances with a precision of around +/- 20mm (at 10m).

The second is the company’s acquisition by the CoStar Group in April 2024 for approximately $1.6 billion. CoStar appears to have cottoned onto the Digital Twin wave and Matterport’s use of AI. The company has backing and resources to double down on its real estate market, as well as entertain addressing a pro market with advanced AI and ever more sophisticated scanners.

Leica BLK 360 G1 vs Matterport Pro3

At $6,000, the Matterport Pro3 costs $15,000 less than the latest Leica BLK360, so it’s not surprising that it can’t match the specifications of Leica’s compact laser scanner. See specs below.

• Matterport Pro3 – 100,000 points per second, spherical capture 20 seconds, +/- 20mm @10m.

• Leica BLK360 G1 – 680,000 points per second, spherical capture in 20 seconds, 4mm @ 10m.

Capture times are roughly the same, but the Leica BLK360 G1 has almost seven times the depth of density of points, and up to five times greater accuracy. Both machines capture very high-resolution spherical imagery and software can combine laser with photogrammetry.

However, the price can’t be ignored and as an opening LiDAR scanner the Matterport Pro3 is a good option. Of course, the entry-level specs may limit its suitability for applications that require higher levels of accuracy.

Understanding the market

To find out more AEC Magazine talked with Stephanie Lin, VP and GM – Operate at Matterport.

Lin is ‘one of us’. She started as a licensed architect in the state of New York and specialised in super tall skyscrapers. She worked for one year at SOM San Francisco, with the eminent Neil Katz and then did seven years at KPF in New York, London and Shanghai.

Post skyscrapers, Lin went in-house for eight years, to work with retail, specifically Michael Kors and Tory Burch. Lin understands the needs of retail and the pro level features that those in AEC design would love to have from low-cost laser scanning.

She explains Matterport’s offering, “With traditional laser scanning, you’re not just buying the hardware, you also have to train the operator in-house to use specialist software.

“Matterport does the registration and the stitching in the back end with our AI, which we’ve been using for 13 years now, so you can imagine, it’s quite well trained at this point.

“We’re trying to identify the friction points of going from design to the tedious task of documenting a space and then the equally tedious task of building up the Revit model.

“We kind of do full end-to-end for that beginning portion, which is get the quick scan, get the point clouds. You can even get a BIM file or a CAD file from us — remove the tedious day to day stuff and have your team go do everything else.”

Lin is convinced that the proprietary Matterport sauce is a major differentiator. That’s the technology that overlays point cloud, mesh and high-resolution images in the back end when processing a file, as she explains, “We’re really addressing the full building lifecycle, because that’s actually where the expensive part is. It’s not digging the hole in the ground, it’s making that space,” she says.

We’re trying to identify the friction points of going from design to the tedious task of documenting a space and then the equally tedious task of building up the Revit model

“We are now very focused on two solutions for the industry, and that’s design / construction management, and then facilities management,” she adds. “We are used time and time again, especially in the documentation stage, for milestone captures and definitely for handover packages. But we need to own it, and we need to put it out that way. And so this is our big focus for the company.”

In terms of technology, Lin explains how Matterport now has automatic dimensions, the ability to merge complex spaces and to copy notes and annotations from one model to the next. “These are workflow challenges that have hindered people from adopting using Matterport technology on a more regular basis,” she says.

Matterport also has a team of service professional scanners, ‘the Uber on demand’ of scanning. If you want a scan, or don’t want to make the camera investment, it’s possible to have someone go and scan with the Matterport Pro3.

Matterport covers 77 countries or more. That means, you could be based in New York, and get something scanned in Cape Town by tomorrow.

Conclusion

Matterport has taken on the mantle of the democratisation of reality capture. It has a usable device at a price point that is certainly in the right ballpark for mass adoption of LiDAR and photogrammetry.

Matterport seems very committed to going ‘Pro’, offering capabilities to AEC design firms and targeting the long tail of digital twin adoption. However, this will require ongoing evolution of the scanner suite, with more connectors to pro design tools and defined workflows.

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Cintoo has launched Cintoo Metaverse, a new product portfolio designed to bring high fidelity reality capture data to an immersive environment.

The Cintoo VR Experience app is one of two initial apps in the Cintoo Metaverse portfolio. The VR app, which runs on Unreal Engine and is cloud-connected to the Cintoo platform, is designed to extend Cintoo’s collaboration and decision-making capabilities.

It allows project managers, engineers and installers to navigate reality models at a true-to-life 1:1 scale on ‘almost any’ VR device, as well as compare as-builts to as-designed by overlaying scans with 3D models.

3D scans and 3D models are all streamed from the Cintoo Cloud in real time, at the same high mesh resolution as the source point cloud thanks to Cintoo’s TurboMesh engine. According to Cintoo, no preparation work or pre-production is required.

To navigate around the reality model, the Cintoo VR Experience is using a technology first introduced to its platform earlier this year.

With the ‘teleportation camera’ users can teleport anywhere in the scene simply by pointing and clicking or navigating between scan set up locations.

In VR, users can create annotations, take measurements and then sync everything back to the Cintoo project.

Issues identified in VR can be exported in BCF Format, or synced with BIM Track, Autodesk BIM 360 or Procore.

“This is not just about visualization; it’s about driving actionable insights, reducing costs, and improving operations,” said Dominique Pouliquen, CEO of Cintoo. “Whether a construction, oil and gas or manufacturing company, the industrial metaverse enables you to harness your data in real-time, creating smarter, more efficient workflows.”

The second Cintoo Metaverse app, 3D Layout Experience, has been developed with Cintoo’s partner, Theorem Solutions. It allows users to navigate a 3D mesh, check clearances when moving equipment, and simulate future workspaces.

Meanwhile, Cintoo has closed a €37 million Series B funding round led by Partech, a global tech investment firm. With the fresh funding, Cintoo will enhance its SaaS platform by expanding its portfolio of integrations and will build on its new industrial metaverse experience and automatic asset tagging capabilities.

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Trimble gave attendees of its annual Trimble Dimensions user conference an inside look at the company’s latest initiatives to incorporate AI into AEC workflows, including visualisation, reality capture, project management and takeoff.

For visualisation, SketchUp Diffusion [Labs] is a generative AI-powered tool, available as part of the SketchUp Labs public beta program, that allows architects and designers to generate visualisations in seconds based on the active SketchUp viewport and a natural language text prompt or preset style.

Learn more about Diffusion models in this guide for AEC professionals).

For project management, ProjectSight includes a new drawing import feature that uses AI to read and extract critical drawing information for improved project visualisation.

For takeoff, Trimble LiveCount uses new AI functionality to automatically detect and count thousands of symbols on construction drawings with a view to saving contractors from hours of manual, repetitive and time-consuming tasks.

Trimble LiveCount includes the ability to automatically detect and count different types of receptacles and switches — the most common electrical items on drawings, helping electrical contractors create estimates faster, easier and more accurately.

Trimble LiveCount AI functionality is available in the Trimble Accubid Anywhere (named user) and Hosted Accubid Classic Estimating Essentials subscriptions.

Meanwhile, Trimble has announced that SketchUp has surpassed one million active subscribers.

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