In the previous article, we explained why a control network is the foundation of a reliable digital copy of an industrial plant and a prerequisite for long-term data consistency. However, this is only the first step. Equally important is how data is acquired in the field.

In 3D laser scanning practice, attention is still often focused on parameters that look good in technical specifications: maximum scanner range, very high resolution, or declared single-scan accuracy. Experience shows, however, that these parameters rarely determine the real usability of the data.

This article is based on the long-term experience of the 3Deling team and on observations gathered by Paweł Dudek, CEO of 3Deling, over nearly two decades of working with 3D laser scanning — from the first technology tests to large, complex industrial projects.

3d laser scanning field measurements

Early 3D Scanning Experiences – A Lesson in Humility

I remember the “tests” of our first scanner — it was 2007. We set a very high scanning resolution, because “it has to be dense” for the data to be good and for nothing to be missed. There was a slight surprise that a single scan would take around 30 minutes, but we waited for the result.

The scan finished, the data was transferred, then the point cloud was “processed” and opened in Pointools View (back then, it wasn’t Bentley Pointools yet). It took a while, but finally there it was — a very “heavy” scan. The data was visible at a very long distance. We could even see a chimney of a heating plant that no longer exists, located several hundred meters away. It was impressive.

This situation took place almost 20 years ago. At the time, each of us already had some experience with 3D laser scanning and we carried out such measurements on a regular basis. Looking from today’s perspective, however, it is clear how much we were still missing back then — especially when it comes to large-scale projects.

Today, our survey teams perform thousands of scans on a single site, all registered within one coordinate system, often under difficult conditions and time pressure. And in the end, only one thing really matters — that the client receives the best possible data.

Why We Scan Differently Today

In practice, the approach to scanning looks very different today. And it is not because we want to scan “fast and carelessly,” close the project and move on. Quite the opposite.

To obtain the most complete and usable geometric representation of an object, the key factor is the number of scans and their placement, not the maximum resolution or range of the scanner.

Scan Resolution – Why “Denser” Does Not Always Mean “Better”

Very dense scans are simply “heavy” datasets. They are harder to work with — both due to software limitations and hardware performance constraints.

That is why individual scans are often filtered and their resolution reduced. As a result, a unified point cloud can be five to six times lighter, while being much more convenient to use — without losing information that is actually relevant for design work.

Scanner Range – A Parameter Rarely Used to Its Full Extent

Most scanners we use have a range well above 100 meters — one of them even up to 600 meters. In practice, however, the data is usually used from much shorter distances:

• indoors: typically up to about 30 m,

• outdoors: typically up to about 50 m.

The full scanner range is rarely utilized and usually only in cases involving very tall structures with no safe physical access.

Completeness of the Geometric Representation – The Key Parameter

This is the most important data quality parameter — and at the same time one that can almost never be achieved 100%. There will always be so-called “shadows” or blind spots — areas with missing data.

However, these can be significantly reduced by performing a large number of scans from different positions, heights, and distances. With hindsight, it is clear that the number of scans is the key factor influencing the quality of the final geometric representation of an object.

Number of Scans and Real Design Work

We often support clients who are preparing for plant digitalization projects in drafting tender specifications. We then see that less experienced investors tend to focus primarily on parameters that look best “on paper”:

• range (the further, the better),

• resolution (the denser, the better),

• accuracy (ideally 1 mm).

We understand this — we used to think the same way ourselves. That is why we try to “demystify” these expectations and draw attention to what truly matters. And that parameter is the number of scans.

Where an object is well covered with scans, with many scan positions and a sensibly planned measurement geometry, subsequent modeling proceeds smoothly. The data is clear, there are no “holes,” elements can be interpreted unambiguously, and the model is created quickly — without guesswork.

In remote projects, for example in the Middle East, insufficient scan coverage becomes a serious issue very quickly. When data is sparse or scans are taken from unsuitable positions, modeling and design work based on point clouds turn into speculation. Information is missing, discontinuities appear, and it is unclear “what is what.” In extreme cases, such data is simply unusable.

Missing Data Means Real Costs

When data is incomplete, problems arise:

• returning to the site to perform additional scans,

• sending someone with a camera to take manual reference photos,

• accepting simplifications and uncertainties in the model.

Each of these options means additional time, cost, and risk of errors.

That is why, in practice, instead of performing a small number of very dense scans, we focus on a large number of scans with slightly lower resolution but good object coverage. This allows us to:

• obtain complete geometric data,

• minimize blind spots,

• create good conditions for modeling and design work,

• significantly reduce the time needed to interpret the data — designers do not have to guess what is where, because everything is already clear at the point cloud stage.

Unified Point Cloud and Working with Data

All scans are combined into a single unified point cloud, usually additionally filtered (e.g. to 5 mm). This unified cloud is used for 3D modeling and further design work.

At the same time, all individual scans with color information and panoramas are preserved and can be accessed at any time — for example via WebPano. This is a major advantage, especially for complex installations, where checking details, heights, and spatial relationships is crucial during design.

What to Look for in a Request for Proposal?

When selecting a 3D scanning provider, it is worth looking beyond hardware specifications.

Not only at:

• resolution,

• range,

• manufacturer-declared scanner accuracy.

But above all at:

• the estimated number of scans for the object.

This is one of the best indicators of the real quality of the data you will receive. A higher number of well-planned scans means fewer uncertainties, faster design work, and real savings in time and cost throughout the entire project lifecycle.

Summary

Resolution and scanner range are important, but they do not determine project success.
The number of scans and their placement have the greatest impact on the quality and practical usability of the final data.

Other important factors include the accuracy of the unified point cloud and a properly defined coordinate system — topics we will cover in the next articles of this series.

Planning 3D laser scanning or industrial plant digitalization?

If you want your data to be complete, consistent, and truly usable for design and engineering, the scanning strategy should be defined before any fieldwork begins.

At 3Deling, we help clients plan the number and placement of scans so that data quality translates into real time and cost savings throughout the project lifecycle.

Contact us to discuss your facility and project requirements.

The post Data Quality in 3D Scanning: Why the Number of Scans Matters More Than Resolution appeared first on 3Deling - Experts in 3D Laser Scanning and Point Cloud Processing.

However, for a digital twin of a plant to be reliable, consistent, and useful over the long term, one essential element is often underestimated at the planning stage: the control network.

Control network and distribution of 3D laser scanning positions within an industrial plant

What is a control network in the context of plant digitalization?

A control network is a set of stable reference points whose positions are precisely defined within an adopted coordinate system, together with information about their accuracy. In practice, it forms the physical reference framework to which all measurements within the plant are related.

In the context of digitalization, this means that the control network:

• defines the geometry and scale of the entire digital documentation,

• allows data from different laser scanning campaigns to be combined,

• enables the integration of point clouds, 3D models, and technical drawings.

Without a properly designed control network, even the highest-quality 3D laser scanning data loses much of its practical value.

Why is a control network critical for 3D laser scanning?

3D laser scanning generates vast amounts of data in the form of point clouds. For this data to be:

• combined into a coherent dataset,

• compared over time,

• used in modernization and expansion projects,

it must be referenced to a single, consistent coordinate system.

The same reference system can then be used not only for as-built surveys, but also for setting out newly designed objects in the field. This ensures that inventory data, design documentation, and construction activities all refer to the same control network, eliminating discrepancies between existing conditions, design intent, and actual positioning on site.

In practice, this significantly reduces interpretation errors, ambiguities in project positioning, and situations where responsibility for inconsistencies becomes blurred between the survey team, designers, and construction contractors.

The control network as the “skeleton” of a digital plant twin

The control network therefore acts as the structural backbone of a digital plant twin. Thanks to it:

• subsequent stages of digitalization can be implemented gradually,

• data collected over different years remains compatible,

• changes within the facility can be measured and clearly quantified.

This is particularly important in industrial plants, where digitalization is a long-term process, not a one-off project.

A local control network tailored to the digital plant

In industrial plant digitalization projects, a local control network is most commonly used. While it may be linked to a national coordinate system, it is optimized for the specific needs of the facility.

This approach offers tangible benefits:

• software used for point cloud processing and 3D modeling works most reliably when objects are described using low, positive coordinates, i.e. relatively small numerical values measured in meters from a local origin,

• the coordinate system can be aligned orthogonally with building and installation axes,

• data becomes more intuitive for designers, engineers, and maintenance teams.

A well-designed control network makes digital documentation easier to use and simpler to expand in the future.

Data stability today and in the future

One of the main objectives of plant digitalization is to preserve and organize technical knowledge, especially in the face of staff turnover and organizational change.

A control network:

• ensures consistency between historical and current data,

• enables comparisons of the facility’s condition at different points in time,

• provides a reference framework for future modernization, expansion, and analysis.

As a result, the digital plant twin is not a static archive, but an active tool supporting everyday technical decision-making.

The control network as the basis for integration with technical documentation

The full value of a digital plant twin emerges when 3D data is integrated with:

• CAD and CAE documentation,

• technological diagrams such as P&IDs,

• operational and maintenance information.

The control network enables this integration by ensuring that all elements refer to one consistent spatial reference system. This translates into:

• faster preparation of modernization projects,

• better communication with design companies,

• reduced risk of execution errors on site.

Summary: why digitalization should start with a control network

A control network is not an optional addition to plant digitalization—it is its foundation. It determines whether:

• data from different time periods remains compatible,

• point clouds become a practical design support tool,

• the digital plant twin remains useful for many years.

When planning 3D laser scanning and the creation of a virtual representation of an industrial plant, it is worth starting with a simple question:
Do we have a solid reference framework for all our data?

The control network is one of the key elements affecting data quality in the digitalization process, but it is not the only one. In the following articles, we will show how factors such as the number and distribution of scans, the accuracy of the registered point cloud, and the overall measurement strategy influence the practical usability of 3D data.

Is your plant ready for digitalization?

If you are planning 3D laser scanning, installation modernization, or the creation of a digital twin of your industrial plant, a control network is the first step that should be planned consciously.

At 3Deling, we support clients throughout the entire plant digitalization process—from:

• the design and establishment of a control network,

• through 3D laser scanning,

• to the integration of data with technical documentation and CAD/BIM environments.

Contact us to discuss the current state of your documentation and the long-term development of your digital plant twin.

The post Control Network – the Foundation of a Digital Twin of an Industrial Plant appeared first on 3Deling - Experts in 3D Laser Scanning and Point Cloud Processing.