I start with three basic questions.

Are the scans correctly aligned?
I check how well neighbouring scan positions fit together and look for registration problems such as duplicated surfaces or ghosting.

Is the point density appropriate?
The dataset needs enough information for the intended purpose, but unnecessary density can make downstream processing more difficult.

Was everything important actually captured?
Occlusion is unavoidable in many environments, but critical missing geometry can become a serious problem if that information is required later.

These checks quickly tell me a great deal about both the quality of the dataset and what may be required during processing.

Before asking how to process the data, understand what is actually in it.

Subscribe now

Yes. To a large extent, the final deliverable determines what kind of point cloud is actually required.

A dataset intended for CAD documentation does not necessarily need the same characteristics as one prepared for detailed mesh generation, BIM, measurements, visualisation or integration into another platform.

Point density is only one consideration.

The required format, dataset size, organisation and downstream software environment also matter. It is important to understand who will use the data next, what information they need to extract and how they will continue working with it.

For this reason, I prefer to think about the final use of the data before deciding what the dataset should look like.

The deliverable should help define the data, not the other way around.

SEE IT IN PRACTICE

For a nationwide tourism office documentation project, processed point clouds formed the basis for CAD floor plans and supplementary Revit visualisations.

Subscribe now

Only to a certain point.

Registration problems and unwanted data can often be corrected or improved during processing. Point clouds can also be cleaned, filtered and optimised for their intended use.

But there is an important limit.

If a critical part of the surveyed environment was not captured in the field, that measurement does not exist in the dataset. Processing cannot recreate reliable spatial information that was never recorded.

This is why field acquisition and processing should not be treated as completely separate stages. Decisions made during capture directly influence what can be achieved later.

Processing can improve captured data. It cannot replace data that was never captured.

SEE IT IN PRACTICE

During mobile scanning of complex cemetery environments, trajectory planning and sufficient overlap were important because vegetation, monuments and narrow spaces created significant occlusion.

Subscribe now

Yes.

A point cloud can look convincing at first glance while still containing technical problems.

One of the first things I check is how well neighbouring scan positions align. Poor registration can create duplicated surfaces or ghosting, meaning that geometry representing the same physical object does not correctly coincide.

Problems can also originate earlier in the workflow. Incorrect field acquisition or positioning issues, including the use of inappropriate RINEX data during PPK processing, can affect the spatial reliability of the resulting dataset.

The opposite is also true. A point cloud may look less impressive because it has a lower point density, but still be technically excellent if the geometry is reliable and the required information can be extracted.

Visual appearance and geometric reliability are not the same thing.

SEE IT IN PRACTICE

In an elevator shaft verification project, registered point cloud data was used to identify and quantify geometric deviations that were preventing installation of the elevator system.

Subscribe now

The Project

The survey covered approximately 2,500 m² of external industrial space. The goal was to determine the geometric relationship between the surrounding structures, crane system, rails and loading connections, and identify where the system deviated and by how much.

The complex geometry made these deviations difficult to establish reliably using conventional surveying methods, while laser scanning preserved the complete spatial relationship in a single measurable dataset.

Capturing a Moving System

The crane had to be captured in several positions, including connections to six loading openings at one storage section.

The facility remained operational, while the crane itself was stopped during each measurement. A technical supervisor coordinated its repositioning between scans.

The complete field survey was carried out independently in one day using a RIEGL VZ-400i, from 31 scan positions.

From Point Cloud to Engineering Data

The scans were registered, cleaned, colourised and optimised in RiSCAN PRO, producing a unified point cloud with approximately 3–5 mm registration accuracy.

The dataset was used to analyse rail alignment, angular deviations, structural distances, loading connections and the geometry of the 36-compartment curing-area connections.

Distances were evaluated at millimetre level and angular deviations to 0.1°. The findings were presented in 15 annotated deviation views.

The Result

The final delivery consisted of a registered colour point cloud in LAS format and 15 graphical deviation analyses.

The measurements provided the design team with the geometric basis needed to adjust the crane and rail system, without an additional site visit.

The project turned complex captured geometry into directly usable engineering information.

Project at a Glance

1 day — Field survey
31 — Scan positions
1.6 billion — Captured points
3–5 mm — Registration accuracy
15 — Annotated deviation views
0 — Additional site visits

The Takeaway

A laser-scanning project does not always need to end in a BIM model.

Here, the point cloud itself became the engineering environment for measurement, analysis and realignment.

Capture → analyse → adjust.

Planning a Complex Industrial Reality Capture Project?

Need accurate existing-condition data for a complex industrial structure? Let’s discuss the right reality-capture approach.

Reality Capture Support →

Point Cloud Processing →

Work With Roland Kriston →

Or start with the project:

LINKEDIN • EMAIL • WHATSAPP

Higher point density is not automatically better.

There are projects where dense point cloud data is important. If the objective is to create a detailed mesh, for example, the additional geometric information can be valuable.

But if the required measurements, geometry or documentation can be reliably extracted from a lower-density point cloud, additional points may simply create a larger dataset.

That affects storage, transfer, processing performance and the ability of downstream systems to work efficiently with the data.

The right point density is therefore always project-dependent.

More points only add value when the project actually needs them.

SEE IT IN PRACTICE

During a digital cemetery mapping project, the processed datasets contained more data than the target platform could efficiently handle. The point clouds were downsampled while preserving the geometry required for the final application.

Subscribe now

A point cloud is useful when it is correctly registered, has the appropriate point density, and contains the information required for the intended project outcome.

A visually impressive or extremely dense point cloud is not necessarily better. If the information required by the project can be reliably extracted from a smaller dataset, additional points may only increase file size and make processing, transfer and downstream use more difficult.

The intended deliverable matters too. A point cloud prepared for detailed mesh generation may require a very different level of density from one used for measurements, CAD documentation or integration into another platform.

For me, the most important question is therefore not how much data the point cloud contains, but whether it contains the right data for what needs to happen next.

A useful point cloud is not the one that looks impressive. It is the one that is prepared for the purpose of the project.

SEE IT IN PRACTICE

In a digital cemetery mapping project, the completed point clouds contained more data than the target platform could efficiently process. I reduced the point density while preserving the geometry required for the final application.

Subscribe now

This is where I document observations, technologies and practical lessons encountered through real-world work in reality capture and geospatial technology.

You’ll find field observations, equipment and workflow notes, technology tests, industry events and ideas worth examining beyond a single project.

Some are technical. Some are exploratory. All come from direct contact with the work.

Think of these Field Notes as a working record of what I encounter, test and learn along the way.

Subscribe now

This section brings together selected projects from my work across reality capture, laser scanning, point clouds and geospatial workflows.

Rather than showing only finished results, each case study focuses on what needed to be solved, why a particular approach was chosen, how the workflow was structured and what the resulting data needed to support.

The projects range from building documentation and engineering verification to mobile mapping and larger spatial datasets. Different environments require different tools and methods, but the principle remains the same:

The workflow should follow the project requirement, not the other way around.

Explore the individual case studies below.

Subscribe now

PROJECT OVERVIEW

A nationwide tourism information office network required consistent spatial documentation across six locations in Hungary.

I independently carried out terrestrial laser scanning, photographic documentation, point cloud processing and floor plan production for tourism information offices in Hajdúszoboszló, Keszthely, Siófok, Sopron, Tokaj and Zalakaros.

The final documentation included colourised point clouds, CAD floor plans and interior and exterior photographic documentation.

PROJECT TYPE: Multi-Site Building Documentation
LOCATION: Hungary
SECTOR: Tourism · 6 Tourism Information Offices
MY ROLE: Field Capture · Registration · Point Cloud Processing · CAD Production · QA/QC
TECHNOLOGY: RIEGL VZ-400i · RiSCAN PRO · Sony A7 III
OUTPUT: Colourised Point Clouds · CAD floor plans · Photographic Documentation

Typical Scan Positions: approximately 15–25 per office
Average Dataset Size: 20–25 GB per office
Required Accuracy: Centimetre level
Project Duration: Approximately one month

THE CHALLENGE

Applying One Workflow Across Six Different Buildings

Although every office belonged to the same nationwide network, each location presented different layouts, furniture arrangements and lighting conditions.

The challenge was to maintain the same technical standard across six independent sites while adapting scanner positions and field decisions to each individual environment.

MY ROLE

For every assigned location, I independently carried out:

• Site coordination

• Terrestrial laser scanning

• Interior and exterior photographic documentation

• Point cloud registration and processing

• Colourisation and quality control

• REVIT/CAD floor plan production

• Supplementary Revit floor plan visualisation

TECHNICAL WORKFLOW

A standardised capture methodology was applied across all six locations using a RIEGL VZ-400i and Sony A7 III.

Each office typically required approximately 15–25 scan positions before the datasets were processed in RiSCAN PRO through registration, cleaning, colourisation and quality contro

Field Capture → Registration → Cleaning → Colourisation → QA/QC → CAD Production

FROM POINT CLOUD TO CAD

Processed point clouds formed the basis for Revit/CAD floor plans produced to the required centimetre-level accuracy.

Supplementary Revit visualisations shown here were created from the same datasets for portfolio presentation only.

THE OUTCOME

Six tourism information offices were delivered as consistent documentation packages comprising colourised point clouds, REVIT/CAD floor plans and photographic documentation.

FINAL DELIVERABLES

Each surveyed location included:

• Registered and colourised point cloud

• CAD floor plans

• Interior photographic documentation

• Exterior photographic documentation

Additional portfolio material

Supplementary Revit floor plan visualisations (not part of the original project deliverables)

WHAT THIS PROJECT DEMONSTRATES

This project demonstrates how a standardised reality capture workflow can produce consistent spatial documentation across multiple independent locations while still allowing field decisions to adapt to each site.

The same principle can support office networks, public buildings and commercial property portfolios where repeatable capture, processing and documentation standards are required across several locations.

TECHNOLOGY USED

Capture Equipment
RIEGL VZ-400i · Sony A7 III

Software
RiSCAN PRO · Autodesk AutoCAD · Autodesk Revit (supplementary portfolio visualisations)

Methods
Terrestrial Laser Scanning · Point Cloud Registration · Point Cloud Cleaning · Point Cloud Colourisation · CAD Floor Plan Production · Interior & Exterior Photographic Documentation · Quality Assurance / Quality Control

PROJECT CONTEXT

Certain project details remain confidential. This case study focuses on my technical contribution to the project.

Planning a Multi-Site Reality Capture Project?

If you are working with multiple locations, point cloud datasets or a reality capture workflow that requires additional specialist support, I can help with defined technical tasks or broader project involvement.

Reality Capture Support →

Point Cloud Processing →

Work With Roland Kriston →

Or start with the project:

LINKEDIN • EMAIL • WHATSAPP

Most people begin by asking which scanner they should use. In my experience, that is rarely the first question. The better question is: What does the project actually need?

Only then does it make sense to decide which technologies, workflows and specialist knowledge will deliver the right result. That idea sits at the heart of how I approach every project.

This article is an introduction to that way of thinking and the starting point of the Reality Capture Knowledge section. As new articles are published, each stage of the workflow introduced here will expand into dedicated technology guides, Field Notes and documented project examples.

Every Project Begins With a Question

Before choosing equipment, planning fieldwork or opening processing software, every project starts with an objective. What information is needed? Who will use it? How accurate does it need to be? What will happen to the data afterwards?

The answers to these questions influence every decision that follows. Two projects may appear similar on the surface yet require completely different workflows because their objectives are different. Understanding the project first almost always leads to better technical decisions later.

Capturing Reality

Reality capture begins in the physical world. The objective is straightforward: to collect reliable spatial information that can be transformed into useful digital data.

Today there are many different ways to capture that information, including terrestrial laser scanning, mobile mapping, SLAM-based systems, photogrammetry, UAV surveys and GNSS-supported workflows. Each has strengths, each has limitations and none of them is automatically the “best” technology. The right choice depends entirely on the project, the environment and the required outcome.

Throughout my career, I have worked across several of these workflows, and one lesson has remained remarkably consistent.

Good data starts with good planning.

The scanner only records what you choose to capture.

From Reality to Data

One of the biggest misconceptions I still encounter is that a point cloud is already the finished product.

It isn’t.

A point cloud is measurement data.

It is the digital representation of a real environment, created from millions or sometimes billions of measured points. That dataset often becomes the foundation for engineering documentation, survey drawings, BIM workflows, mesh generation, inspection, measurements, visualisation or digital twins.

The point cloud itself is rarely the final objective. It is one stage within a much larger process, and understanding that difference changes the way projects are planned from the very beginning.

Processing Is Where Information Begins to Take Shape

Collecting data is only one stage of a reality capture project. Once fieldwork is complete, the dataset needs to be transformed into something reliable, organised and usable.

Depending on the project, processing may include:

• Registration

• Georeferencing

• Cleaning and optimisation

• Quality assurance and quality control (QA/QC)

• Dataset organisation

• Classification

• Export preparation

• Deliverable preparation

This is where separate scans become a coherent dataset, errors are identified, data quality is verified and information is prepared for the people who will use it next. It is also one of the areas where I most often support project teams as an independent reality capture specialist.

Deliverables Are the Real Objective

Technology is rarely the final deliverable. Clients are not looking for a scanner, or even for a point cloud. They are looking for reliable information that supports the next stage of their project.

Depending on the objective, that deliverable may be a clean point cloud, a mesh, CAD drawings, BIM-ready data, measurements, technical documentation or something entirely different. Understanding the final objective helps determine every earlier decision in the workflow.

The deliverable should shape the process and not the other way around.

Choosing the Right Workflow

Modern reality capture offers more choices than ever before, which makes experience increasingly valuable. More data is not always better data. The newest technology is not automatically the right technology, and the fastest workflow is not always the most efficient once processing, quality control and project requirements are taken into account.

Good decisions come from understanding how the different parts of the workflow influence one another. Field decisions affect processing, processing decisions affect usability, and dataset structure affects downstream workflows. Every stage has consequences for the next.

That wider perspective has shaped the way I approach projects throughout my career.

An Evolving Technology Map

Reality capture continues to evolve. New sensors appear, software improves, automation becomes more capable and AI is beginning to reshape parts of the workflow.

The technologies will continue changing, but the underlying questions remain remarkably consistent: what needs to be captured, what level of accuracy and information is required, how should the data be processed and what does the final deliverable need to support?

Reality Capture Knowledge will continue to expand around these questions, with dedicated articles, practical guides, Field Notes and real project examples covering individual technologies and workflow stages.

The Workflow at a Glance

Every completed project improves the next workflow.

Explore Reality Capture Knowledge

This article is the starting point.

From here, the knowledge base continues through three complementary types of content:

Reality Capture Knowledge →
Technical articles exploring the technologies, methods and decisions behind modern reality capture workflows.

Field Notes →
Shorter observations, technology encounters and practical lessons from field work and industry experience.

Projects →
Documented case studies showing how technologies, workflows and technical decisions are applied to real projects.

Whether you are new to reality capture or already working with spatial data, I hope these resources help you understand not only the technologies themselves, but also the thinking behind using them effectively.

Reality capture is not about the scanner. It is about understanding the entire workflow.

Let’s stay connected.

RdesignR is where I share projects, technologies, lessons and practical experience from my work in reality capture and spatial data.

If you work in this field, are exploring these technologies, or simply find this world as interesting as I do, I’d be glad to have you along.

LINKEDIN • EMAIL • WHATSAPP

Subscribe now

PROJECT OVERVIEW

A newly constructed residential building in Budapest encountered a critical problem during elevator installation: the completed shaft geometry differed from the design, leaving insufficient clearance for the elevator system to be installed.

The shaft had been constructed by two consecutive contractors, and deviations introduced during the construction process resulted in a slight geometric twist through the structure.

A complete three-dimensional survey was required to determine the actual as-built geometry, locate and quantify the deviations, and provide reliable dimensional information for corrective construction work.

Using terrestrial laser scanning and point cloud analysis, the physical installation problem was converted into measurable spatial evidence that could be used to determine where the structure required modification.

PROJECT TYPE: As-built Geometry Verification
LOCATION: Budapest, Hungary
SECTOR: Residential Construction
MY ROLE: Field Survey · Laser Scanning · Point Cloud Registration · Data Processing · Deviation Analysis · Technical Documentation
TECHNOLOGY: RIEGL VZ-400i · RiSCAN PRO
OUTPUT: Registered Point Cloud · Geometric Deviation Report
APPLICATION: As-built Verification · Construction QA
COLLABORATION: Connect-King Kft

THE CHALLENGE

During the installation phase, it became apparent that the elevator system could not be installed within the completed shaft.

Two contractors had worked sequentially on the structure, and the resulting as-built geometry deviated from the original design. The shaft showed a slight geometric twist, creating insufficient clearance in critical areas.

The key questions were therefore:

• Where did the completed shaft deviate from the design geometry?

• How significant were the deviations?

• Which areas were preventing the elevator installation?

• Where would corrective construction work be required?

Conventional isolated measurements would not provide a sufficiently complete representation of the shaft geometry. A comprehensive three-dimensional survey was required.

MY ROLE

I was responsible for the complete reality capture and analysis workflow, from field acquisition through to the final technical outputs.

My work included terrestrial laser scanning of the elevator shaft, point cloud registration and processing, geometric analysis of the completed structure, evaluation of dimensional deviations and preparation of the final technical documentation.

The objective was not simply to document the shaft, but to transform the captured spatial data into actionable engineering information that could support corrective construction work.

The final deliverables consisted of the registered point cloud dataset and a geometric deviation report showing the actual differences within the completed shaft.

TECHNICAL APPROACH

Terrestrial laser scanning was selected to capture the as-built geometry of the shaft as a complete three-dimensional dataset.

The confined and vertically extended environment required multiple scanning positions to achieve sufficient coverage of the structural surfaces and minimise areas of missing data.

The survey was performed using a RIEGL VZ-400i terrestrial laser scanner. Individual scans were subsequently registered and processed in RiSCAN PRO to create a unified spatial representation of the completed shaft.

The resulting registered point cloud provided the geometric basis for detailed verification and deviation analysis.

POINT CLOUD ANALYSIS

The registered point cloud enabled the completed shaft to be analysed as a continuous three-dimensional structure rather than as a series of isolated measurements.

This made it possible to identify the geometric relationship between different sections of the shaft and determine where the as-built structure was restricting the space required for elevator installation.

The analysis identified:

• horizontal deviations,

• vertical deviations,

• dimensional inconsistencies,

• geometric twisting through the shaft,

• and areas with insufficient installation clearance.

The measurements shown in the analysis represent the actual deviations identified from the survey data and provided a quantitative basis for determining where corrective work was required.

FROM SCAN DATA TO ENGINEERING EVIDENCE

Capturing the shaft was only the first stage of the project.

The critical task was converting the registered point cloud into information that could explain why the elevator could not be installed and where the completed structure differed from the required geometry.

By analysing the spatial relationship between the shaft surfaces, the geometric distortion could be located and documented rather than relying on assumptions or individual manual measurements.

The resulting deviation information gave the construction team a spatially referenced basis for planning the required structural corrections.

THE OUTCOME

The analysis confirmed that the completed elevator shaft deviated from the intended geometry and identified the areas responsible for the installation conflict.

Based on the point cloud data and deviation report, sections of the shaft were locally removed to create the clearance required for the elevator installation.

This provided a clear end-to-end result:

physical installation problem → 3D survey → geometric analysis → corrective construction → successful installation

DELIVERABLES

The project delivered two primary technical outputs:

• Registered Point Cloud Dataset — a complete spatial representation of the surveyed elevator shaft.

• Geometric Deviation Report — documented measurements identifying the actual geometric differences within the completed structure.

These outputs provided both the underlying spatial dataset and the engineering evidence required to support corrective work.

WHAT THIS PROJECT DEMONSTRATES

This project demonstrates how terrestrial laser scanning and point cloud analysis can turn a complex construction problem into measurable engineering evidence.

Rather than relying on isolated manual measurements, the registered point cloud provided a complete representation of the as-built shaft geometry. This made it possible to identify the deviations responsible for the installation conflict and support targeted corrective work.

The same approach can support construction verification, structural QA and other projects where complex as-built geometry must be understood before installation, modification or corrective work.

TECHNOLOGY USED

Hardware:
RIEGL VZ-400i

Software
RiSCAN PRO

Methods
Terrestrial Laser Scanning (TLS) · Point Cloud Registration · Point Cloud Processing · Geometric Deviation Analysis · As-built Geometry Verification · Construction QA

Survey equipment provided within the Connect-King Kft. project collaboration.

Need Reality Capture Support for a Complex Project?

I support projects involving terrestrial laser scanning, point cloud processing, geometric analysis, QA/QC and technical reality capture workflows.

If your project involves complex as-built conditions, verification or spatial data that needs to support an engineering decision, we can start with the project requirement.

Reality Capture Support →

Point Cloud Processing →

Work With Roland Kriston →

Or start with the project:

LINKEDIN • EMAIL • WHATSAPP

This field recording shows mobile SLAM scanning with a Lixel L2 Pro during a digital cemetery mapping project in Hungary.

The cemetery environment was captured using continuous mobile scanning trajectories rather than fixed terrestrial scan positions. Survey areas were planned with sufficient overlap between neighbouring trajectories to support subsequent point cloud registration and integration.

The environment presented several typical challenges for mobile reality capture, including narrow passages, dense vegetation, trees, irregular burial plot layouts and partially obscured graves.

GNSS-measured control points were used within the wider survey workflow to support georeferencing and refinement of the scanner-derived EOV coordinates.

From Capture to Point Cloud

Field acquisition was only the first stage of the workflow.

Individual trajectory datasets were processed and cleaned in Lixel Studio, then aligned and integrated in RiSCAN PRO. The resulting point clouds were georeferenced, colourised, optimised and checked against the project’s centimetre-level spatial requirements.

The final point density was also reduced to meet the technical requirements of the target cemetery management environment while preserving the spatial information required for documentation.

Selected captured environments were additionally processed into 3D Gaussian Splatting (3DGS) models using LCC Studio (Lixel Cyber Color).

Capture → Processing → Registration → Georeferencing → Optimisation → QA/QC → Delivery

Technology

Mobile Laser Scanning: Lixel L2 Pro
GNSS: Emlid Reach RX2 / RS4
Point Cloud Processing: Lixel Studio
Registration & Integration: RiSCAN PRO
Spatial Reference: EOV
3DGS: LCC Studio (Lixel Cyber Color)

This field recording is part of a larger digital cemetery mapping project covering three cemetery sites in Hungary. I worked within a five-person project team in collaboration with RoaData Kft., contributing to field acquisition and the downstream reality capture and point cloud workflow.

🟠 Read the full Digital Cemetery Mapping case study:

Let’s connect.

LINKEDIN • EMAIL • WHATSAPP

Subscribe now

PROJECT OVERVIEW

Three municipal cemetery sites across two rural municipalities in Hungary were digitally documented to support cemetery mapping and the online registration of burial plots.

I worked as part of a five-person project team in collaboration with RoaData Kft., contributing specifically to field acquisition and the downstream point cloud workflow.

Multiple mobile scanning trajectories had to be processed, registered, georeferenced, cleaned and optimised into unified datasets suitable for the target cemetery management environment.

PROJECT TYPE: Municipal Spatial Documentation
MY ROLE: Capture · Processing · Registration · Georeferencing · QA/QC
OUTPUT: Platform-Optimised Point Clouds · 3DGS
COLLABORATION: RoaData Kft.

THE CHALLENGE

Cemetery environments contain dense and irregular geometry. Narrow pathways, mature vegetation, monuments and closely positioned graves create significant occlusion during mobile data capture.

The project also introduced processing challenges. Independently captured parcels required sufficient overlap for reliable registration, while the final point clouds had to maintain geometric detail and positional accuracy within the technical limitations of the target platform.

The challenge was therefore not simply to capture as much data as possible, but to create spatial datasets that were accurate, coherent and usable for their intended application.

MY ROLE

I was responsible for the reality capture and point cloud workflow within the five-person project team, covering assigned field acquisition and downstream data processing.

My responsibilities included mobile scanning, trajectory overlap planning, point cloud cleaning, parcel integration, registration in RiSCAN PRO, GNSS-supported georeferencing refinement, colourisation, point density optimisation and final QA/QC.

My involvement therefore extended from field capture through to the preparation of project-ready spatial datasets.

Other project team members handled downstream system integration and cemetery management platform tasks after the processed datasets were delivered.

THE TECHNICAL APPROACH

Each cemetery was divided into smaller survey parcels and captured using handheld mobile laser scanning.

Because the workflow relied on continuous trajectories rather than fixed scan positions, sufficient overlap between neighbouring survey areas was essential for reliable downstream registration.

GNSS-measured control points provided an independent spatial reference for refining scanner-derived EOV coordinates.

The capture strategy was therefore designed around the requirements of the processing workflow:

Capture → Maintain Overlap → Establish Control → Register → Georeference

Dense vegetation and narrow spaces required slower, more deliberate scanning trajectories. The same conditions increased downstream cleaning effort by generating significant unwanted point cloud data around burial plots.

POINT CLOUD PROCESSING

Individual trajectories were processed in Lixel Studio and transferred to RiSCAN PRO for registration and integration. Overlapping geometry between neighbouring survey areas supported alignment, while GNSS control points were used to refine the scanner-derived EOV coordinates.

Processing → Cleaning → Alignment → Registration → Georeferencing → Colourisation → Optimisation → QA/QC

The objective was to transform independent scan datasets into coherent, project-ready spatial data.

DATA OPTIMISATION FOR THE TARGET PLATFORM

The completed point clouds contained more data than the target platform could efficiently process. The datasets were therefore filtered and downsampled while preserving the geometry required for practical documentation.

Approximately 1 TB of processed point cloud data was handled across the project.

The highest possible point density is not always the most useful deliverable.

ADDITIONAL 3D VISUALISATION

Selected environments were also represented using 3D Gaussian Splatting (3DGS).

While point clouds remained the technical reference dataset, 3DGS provided a more intuitive visual layer for non-technical stakeholders.

THE OUTCOME

The workflow transformed multiple independent mobile scanning trajectories into unified, georeferenced and platform-optimised spatial datasets.

• Registered and integrated point clouds

• Cleaned and colourised datasets

• EOV georeferenced data

• Platform-optimised point clouds

• 3DGS visualisations

WHAT THIS PROJECT DEMONSTRATES

This project demonstrates that the value of reality capture is created not only during field acquisition, but throughout the processing workflow that follows.

Registration, cleaning, georeferencing, QA/QC and optimisation were essential to transform fragmented mobile scanning trajectories into coherent, project-ready spatial datasets.

The same principle applies to teams that already have captured data but need specialist processing capacity to turn it into reliable, usable project information.

TECHNOLOGY USED

Hardware
Lixel L2 PRO · Emlid Reach RX2 · Emlid Reach RS4

Software
Lixel Studio · RiSCAN PRO

Methods
Mobile Laser Scanning · GNSS Georeferencing · Registration · Optimisation · QA/QC · 3DGS

Survey equipment provided within the RoaData Kft. project collaboration.

Working With Complex Point Cloud Data?

I support projects that require registration, cleaning, georeferencing, optimisation, QA/QC and preparation of project-ready spatial datasets.

If your team already has captured data and needs specialist processing capacity, we can start with the dataset and the intended project outcome.

Reality Capture Support →

Point Cloud Processing →

Work With Roland Kriston →

Or start with the project:

LINKEDIN • EMAIL • WHATSAPP

Every visit to INTERGEO is a reminder of how quickly the geospatial and reality capture industry continues to evolve.

What I find most interesting about events like this isn’t simply seeing the latest scanners, sensors and software. It’s seeing how individual technologies are becoming part of increasingly connected workflows from data acquisition and processing to analysis and final delivery.

After more than 15 years working with 3D laser scanning, point clouds and geospatial data, I still find it valuable to explore new approaches, compare technologies and talk to people solving similar problems in different ways.

Because new technology alone is rarely the answer.

A faster scanner, a different sensor or more automated software can open up new possibilities. But whether a technology is actually useful depends on the problem we are trying to solve and the output the project requires.

That is probably one of the reasons I still enjoy events like INTERGEO. There is always something new to see, question, compare and learn from.

The technology keeps evolving.

The important question remains the same:

What does the project actually need?

Let’s stay connected.

RdesignR is where I share projects, technologies, lessons and practical experience from my work in reality capture and spatial data.

If you work in this field, are exploring these technologies, or simply find this world as interesting as I do, I’d be glad to have you along.

Explore: Reality Capture Knowledge · Field Notes · Projects

LINKEDIN • EMAIL • WHATSAPP

Subscribe now

After completing the required drone training and examinations, I had the opportunity to gain hands-on experience with the WingtraRAY fixed-wing UAV during a field demonstration for Hungary’s national railway company, in cooperation with RoaData.

Coming from terrestrial laser scanning, mobile mapping and point cloud processing, what interested me most was seeing another approach to geospatial data acquisition in the field.

Every capture technology has its strengths and limitations. The important part is not simply adding another tool to the list, but understanding where it fits into a wider workflow and what problem it can actually solve.

For me, that process of testing, comparing and learning remains an important part of working with reality capture technology even after more than 15 years in the field.

Different tools. Different perspectives. One connected workflow.

Let’s stay connected.

RdesignR is where I share projects, technologies, lessons and practical experience from my work in reality capture and spatial data.

If you work in this field, are exploring these technologies, or simply find this world as interesting as I do, I’d be glad to have you along.

Explore: Reality Capture Knowledge · Field Notes · Projects

LINKEDIN • EMAIL • WHATSAPP

Subscribe now

I have always wanted to understand how things work.

As a child, that often meant taking things apart. Most of the time, I even managed to put them back together. :)

When I was around ten, I completely dismantled an old mechanical watch because I wanted to see what was happening inside it.

I almost managed to put it back together.

Almost. :)

It never worked again, but I understood it better than I had before.

Looking back, perhaps that says something about the way I still approach technology today.

I want to understand what is happening beneath the surface, how something works, what it can do, where its limitations are and, most importantly, what problem it can actually solve.

Today, that curiosity sits at the centre of my work in reality capture, geospatial technology and point cloud processing.

Growing up between two worlds

I belong to a generation that grew up largely in an analogue world and then watched everyday life gradually become digital.

My childhood was spent outside, cycling around with my best friend, playing football, spending entire days with friends and coming home when it was time.

There were no smartphones constantly competing for our attention.

Then technology slowly started appearing everywhere: mobile phones, colour televisions, video games, personal computers and eventually the internet and smartphones.

I became fascinated by computers too although simply using them was rarely enough.

I wanted to know what was inside them. So I took them apart, changed components and eventually started building my own.

Later, video games opened another window into what digital environments could become.

I still remember walking out of the cinema after seeing The Matrix for the first time. I was completely fascinated.

But despite my interest in digital technology, I have always been more interested in the real world.

Perhaps that is one reason reality capture eventually felt so natural to me.

It sits directly between the two.

The physical world becomes digital information.

My first millions of points

My introduction to reality capture came in 2009.

The company I was working for at the time was expanding into a new area and invested in mobile laser scanning.

The system was a RIEGL VMX-450.

That was my first real encounter with this technology.

And what fascinated me most was the point cloud: suddenly, buildings, roads, infrastructure and entire environments could be represented through millions of spatially positioned points.

My early projects included municipal surveys across Hungary, eventually involving the complete mobile laser scanning of around one hundred settlements.

It was a completely different approach to mapping from what I had known before.

But what interested me wasn’t only the capture technology. It was what happened afterwards. The point cloud. The processing. The possibilities hidden inside the data. How something captured in the physical world could be transformed into structured spatial information.

More than fifteen years later, it still interests me.

Over the years, mobile laser scanning led me into terrestrial laser scanning, and from there into different technologies, processing workflows and project environments.

The tools have changed enormously, so has what we can do with them.

But the part I enjoy has remained remarkably consistent.

I still enjoy the planning, being in the field, seeing a point cloud come together for the first time and processing the data. But what I particularly enjoy is solving problems.

A difficult registration that finally works. An unexpected situation in the field that requires a different approach. A dataset where something doesn't look right and you have to understand why. Or helping someone who has been stuck with a technical problem and finding the solution together.

Those situations force you to reach back into everything you have learned and experienced over the years.

To test ideas. To question assumptions. To try another approach.

For me, that is still one of the most satisfying parts of the work.

Think first. Then decide.

I tend to think carefully before making important technical decisions. I like to understand the situation, check again, consider the alternatives and then decide. Professionally, that habit has served me well.

But planning also means knowing when the original plan should change. I have arrived on site with scanning positions carefully planned in advance, only to realise once I could actually see the environment that there was a more efficient approach. So I changed the plan.

Experience is not about following the original workflow at all costs. Sometimes it means recognising when it is better to rethink it.

Good work starts with honest collaboration.

I enjoy working with people who are straightforward, know their field and are willing to discuss problems openly.

For me, good professional relationships are built on mutual trust, respect and direct communication: ask questions, make suggestions, discuss problems and, if something isn't working, say it and explain why.

I would always rather discuss a problem openly than pretend it isn’t there.

Perhaps the strongest confirmation of that approach is when a client comes back with another project. Not simply because it means more work, but because returning means that what I delivered professionally and personally created enough trust to work together again.

That matters to me.

→ Project Collaboration

Why RdesignR?

After more than fifteen years, I reached a point where I started thinking differently about how I wanted to use everything I had learned.

My experience has never belonged to just one narrow part of reality capture.

I have worked across technologies, field acquisition, processing, spatial data and different types of project environments.

Some areas I know deeply, others I continue to develop.

I didn’t want that experience to remain confined to one role, one company or one narrowly defined part of the workflow.

I wanted the freedom to apply it across different projects, clients, technologies and industries. That is one of the reasons I created RdesignR.

The R comes from Roland.
And DESIGN reflects something I have increasingly realised about this work: reality capture is also a form of design.

We design workflows and ways of capturing environments, structure information and make technical decisions around the outcome a project needs.

RdesignR gives me a place to bring those different parts together.

Today, it allows me to work independently with different clients and projects while continuing to build my expertise.

And over time, I want it to become something broader around that knowledge. But it starts with the experience I already have and the work I can do today.

Independence means more than working for yourself.

After many years as an employee, independence has changed the way I think about my professional life.

Yes, it means having more control over which projects I work on, who I work with and how I organise my time.

But for me, it goes further than that.

It means not being defined exclusively by one company or one role. It gives me the opportunity to work internationally, build relationships with different clients and specialists, apply my experience across different industries and project environments, and continue developing.

Since starting to work independently, I have increasingly felt that this way of working suits me.

Why share what I know?

I don’t necessarily think of myself as someone who simply wants to “write articles”.

What I want is to share knowledge.

Over the years, people have sometimes called me because they were stuck with a technical problem like a registration, a dataset, a workflow or simply something that wasn't behaving as expected.

Several times, I have received feedback afterwards that a conversation or suggestion saved them hours of struggling with the problem.

I’ve even been told: “You should do this professionally.”

That stayed with me.

There is a lot of knowledge that accumulates after working in a technical field for fifteen years. Some of it can be found in manuals. Some can be learned in training. But a lot of it comes from projects that worked, projects that didn’t, mistakes, unexpected situations and finding solutions under real conditions.

That is the kind of knowledge I want to share through RdesignR.

I want to share not only what works, but also what doesn't, what could be improved, where technologies have limitations and what practical experience teaches you that a product brochure usually doesn't.

If that helps someone solve a problem, understand the profession better or perhaps helps a younger person find their way into this field, then it has value.

Explore more: Reality Capture Knowledge · Field Notes

Still learning.

Fifteen years of experience doesn’t mean there is nothing left to learn.

Quite the opposite.

New capture technologies, automation and AI are continuing to change what is possible, and I am interested in understanding where they genuinely improve technical workflows and where they don’t.

The tools will continue changing and the workflows will continue evolving. There will always be another problem to understand, another technology to test and another project that requires a different approach.

Perhaps that ten-year-old who dismantled the watch never really disappeared.
I just have slightly more complicated things to take apart now.

If I could give my younger self one piece of advice...

I would tell him not to stay inside the safety of traditional employment for too long simply because it feels secure.

Learn languages.

Build relationships.

Meet people outside your immediate professional environment.

Be brave enough to explore new directions.

Technical knowledge matters enormously.

But over a career, the relationships you build and your ability to communicate that knowledge beyond your immediate surroundings can become just as valuable.

It took me more than fifteen years to reach the point where I was ready to bring those things together.

RdesignR is the next step.
And I’m still curious about where it will lead.

Let’s stay connected.

RdesignR is where I share the projects, technologies, lessons and practical experience behind my work in reality capture and spatial data.

If you work in this field, are exploring these technologies, or simply find this world as interesting as I do, I’d be glad to have you along.

Explore: Projects · Reality Capture Knowledge · Field Notes

LINKEDIN • EMAIL • WHATSAPP

Subscribe now

Through RdesignR, I support surveying, geospatial and reality capture teams with specialist processing, technical expertise and project-based reality capture support.

From captured data to project-ready point clouds

Reality capture projects do not end when the data is captured.

Registration, cleaning, optimisation, georeferencing, quality control and dataset preparation can quickly become a bottleneck, especially when project volumes increase, deadlines overlap or internal processing capacity is stretched.

That is where I can step in.

Through RdesignR, I provide project-based and ongoing point cloud processing support for teams that already work with reality capture data but need additional experienced capacity to move projects forward.

This can mean taking responsibility for a defined processing task, supporting an existing internal team, troubleshooting a difficult dataset or helping structure a more efficient workflow.

Point Cloud Processing & Reality Capture Support

Support can be provided remotely and integrated into your existing project workflow.

Typical areas of support include:

• Point cloud registration and registration review

• QA/QC

• Point cloud cleaning and optimisation

• Large dataset organisation and handling

• Georeferencing support

• Dataset, export and deliverable preparation

• Processing troubleshooting

• Reality capture workflow optimisation

• Project-specific technical support

The exact scope depends on the project, the available input data, the software environment and the required output.

Some projects need support with one specific dataset.

Others need additional processing capacity during a busy period.

And some teams need an experienced external specialist they can call on whenever the workload exceeds their internal resources.

That's the kind of work I built RdesignR around.

Additional capacity. Without additional permanent headcount.

Reality capture workloads are rarely constant.

A surveying company may suddenly have several large projects running at the same time.

A field team may be capturing data faster than the internal processing team can deliver it.

A complex dataset may require additional expertise.

Or a deadline may simply leave no room for a processing backlog.

Hiring permanent staff is not always the right solution.

Through RdesignR, I provide a flexible alternative.

Start with one project.

No unnecessary complexity. No long-term commitment required to see how we work together.

Reality Capture Field Support

Not every project can be handled remotely.

I am also available for selected project-based field assignments in Hungary and across Europe.

This may include:

• Terrestrial laser scanning

• Mobile mapping

• GNSS-supported workflows

• Reality capture fieldwork

• Site documentation

• Data acquisition support

• Project-specific capture workflows

Field assignments can be delivered using client- or partner-provided equipment, allowing the technology to be selected around the requirements of the project rather than around a fixed equipment inventory.

The technology is selected around the requirements of the project not around a fixed equipment inventory.

Experience across the full reality capture workflow

Over the past 15+ years, my work has included:

• Built environment documentation

• Buildings and technical facilities

• Large-scale point cloud datasets

• Cemetery mapping

• Educational facility surveys

• Tourism and heritage environments

• Spatial documentation

• Registration and georeferencing

• Point cloud optimisation

• Mesh and 3D spatial model production

• Multi-technology reality capture workflows

Reality capture is not simply about operating a scanner or processing a point cloud.

It is about understanding the relationship between the site, the technology, the data, the workflow and the final deliverable.

That wider perspective is at the core of how I work.

Every reality capture project is different. My role is to understand the technical challenge first, then determine the most appropriate workflow, technologies and level of support required.

An Independent Reality Capture Practice

RdesignR is not built around a fixed fleet of equipment.

It is built around technical expertise, practical experience and selecting the right workflow for each project.

For remote processing projects, I can integrate into existing reality capture workflows and provide additional technical capacity where it is needed.

For field projects, I regularly work alongside clients and trusted project partners whenever additional expertise or project requirements call for it.

RdesignR is my independent professional practice, bringing together technical expertise, project experience and the flexibility to adapt to each project's requirements.

One specialist when that is enough.

The right collaborators when the project demands more.

Selected Projects

Real projects are the best way to understand how I work.

Explore selected reality capture and spatial data projects, including the technical challenges, workflows and decisions behind them.

VIEW SELECTED PROJECTS →

Field Notes

Practical observations, lessons and technical insights from my work with reality capture technology, spatial data and point cloud workflows.

READ FIELD NOTES →

Let’s work together.

Your team may already have the technology, the field capacity and the data.

What you need might simply be experienced specialist capacity to help move the project forward.

If you’re working with a complex point cloud dataset, facing a processing backlog or planning an upcoming reality capture project, let’s talk.

LINKEDIN • EMAIL • WHATSAPP

Subscribe now