Project Overview

Replacing 75 corroded steel plates on a pedestrian bridge required more than conventional measurements. Each plate had a unique geometry, with irregular edges and non-standard angles, making accurate reproduction particularly challenging.

I was commissioned to capture their geometry using terrestrial laser scanning and prepare individual CAD drawings for manufacturing.

The contractor removed, numbered and transported the plates to a warehouse, where I carried out the survey.

Project Details

• Industry: Bridge Maintenance / Steel Fabrication

• Application: Reverse Engineering / Scan-to-CAD

• Equipment: RIEGL VZ-400i

• Software: RiSCAN PRO / Autodesk AutoCAD

• Survey: 75 steel plates / 25 scan positions

• Deliverables: 75 individual DWG files

• My Role: Survey Planning, Laser Scanning, Point Cloud Processing, Geometric Reconstruction and CAD Production

The Challenge: Capturing Complex and Irregular Geometries

The main challenge was capturing the complex geometries of 75 steel plates, many featuring irregular outlines, non-perpendicular edges and unique shapes.

Some plates also had deformed edges, damaged corners or missing sections caused by deterioration. Even small geometric inaccuracies could lead to fitting problems during installation.

The objective was to accurately capture each plate, reconstruct damaged sections where necessary, and produce reliable CAD outlines for manufacturing.

The scanning strategy also had to ensure clear separation between the plates and the warehouse floor.

Survey Planning and Field Preparation

Before scanning, I arranged for all 75 steel plates to be placed side by side in the warehouse, leaving small gaps between them. The entire arrangement was captured as a single dataset rather than scanning each plate individually.

The plates were slightly elevated using long, rail-like supports to improve edge visibility and simplify point cloud processing.

This allowed me to use the Height Filter in RiSCAN PRO to separate the plates from the warehouse floor more efficiently.

Careful survey preparation significantly improved the subsequent data processing workflow.

Terrestrial Laser Scanning with the RIEGL VZ-400i

I captured the complete arrangement from 25 scan positions using the RIEGL VZ-400i terrestrial laser scanner.

To obtain a better viewing angle towards the horizontal surfaces and their edges, I adjusted the scanner tripod to its maximum practical height.

I also distributed the scan positions around the entire arrangement, allowing the plates to be captured from multiple directions.

This approach helped minimise occlusions and improved the visibility of the individual contours.

The result was a comprehensive point cloud dataset containing the geometry of all 75 steel plates.

Point Cloud Registration and Processing

The acquired data was processed in RiSCAN PRO.

The first stage involved automatic registration of the individual scan positions, followed by Multi Station Adjustment (MSA2) to refine their alignment.

Once the registration was complete, I performed several point cloud cleaning operations, including reflectance-based and deviation-based filtering.

The elevated arrangement of the plates also made height-based filtering particularly useful for separating the relevant surfaces from the warehouse floor.

After completing registration and cleaning, the point cloud was ready for geometric extraction.

The workflow followed a clear sequence:

Laser Scanning → Automatic Registration → MSA2 → Point Cloud Cleaning → Contour Extraction

From Point Cloud to CAD Geometry

Using the Create New Polyline function in RiSCAN PRO, I traced the individual contours of all 75 steel plates directly from the registered point cloud.

Each polyline was exported separately in DXF format and opened in Autodesk AutoCAD for final processing.

I converted the outlines into two-dimensional geometries by setting all vertices to Z = 0 and ensuring that each contour formed a closed polyline.

Finally, I saved each element as an individual DWG file, maintaining its original identification number for manufacturing.

Geometric Reconstruction: When the Measured Shape Was Not Enough

One of the key challenges was reconstructing damaged or incomplete plate geometries. Some elements had deformed edges, damaged corners or missing contour sections that could not simply be traced from the point cloud.

Using adjacent undamaged edges as references, I reconstructed missing sections and corrected corners where the original right-angle geometry was clearly identifiable.

These adjustments were made in AutoCAD, using the point cloud as the primary reference.

The goal was not to redesign the plates, but to combine accurate measurements with practical experience to produce reliable geometries for manufacturing.

Laser scanning captures the existing geometry. Turning that information into manufacturing-ready CAD data requires additional technical judgement.

Quality Control and Geometric Verification

Every steel plate drawing underwent individual geometric verification. I compared multiple dimensions measured directly in the point cloud with the corresponding AutoCAD geometry.

I also verified that each contour formed a closed polyline with all vertices positioned at Z = 0. AutoCAD’s area calculation provided an additional check of the closed planar geometry.

No DIM annotations were required, as the contractor needed accurate CAD outlines containing the actual shapes and dimensions for manufacturing.

Results and Deliverables

The final delivery consisted of 75 individual AutoCAD DWG files, each representing a numbered steel plate and its reconstructed geometry.

The contractor used these drawings to manufacture and successfully install the replacement plates without geometric fitting problems.

The client was fully satisfied with the results, demonstrating the practical value of accurate laser scanning and Scan-to-CAD workflows for complex steel components.

Final Deliverables

• 75 individual DWG drawings.

• Geometric outlines corresponding to the original plate identification numbers.

• Closed, two-dimensional CAD polylines.

• Reconstructed contour sections where the original geometry was damaged.

The registered point cloud served as the source for geometric extraction and verification, while the final contractual delivery consisted of the individual DWG files.

What This Project Demonstrates

This project demonstrates how terrestrial laser scanning can support reverse engineering and the manufacturing of replacement steel components.

Success depended not only on the scanning technology, but also on careful survey planning, efficient point cloud processing, geometric reconstruction and individual quality control.

The key lesson is that reliable Scan-to-CAD work goes beyond converting points into lines. It requires technical experience to interpret complex geometries and deliver accurate, practical data for manufacturing.

Accurate measurement is the starting point. A usable engineering deliverable is the objective.

Technology and Methods

Equipment: RIEGL VZ-400i Terrestrial Laser Scanner

Software: RIEGL RiSCAN PRO, Autodesk AutoCAD

Methods: Terrestrial Laser Scanning, Automatic Registration, Multi Station Adjustment (MSA2), Point Cloud Filtering, Polyline Extraction, Geometric Reconstruction, Scan-to-CAD, Reverse Engineering, CAD Quality Control.

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www.rdesignr.com

A rail-mounted crane system within an industrial foam production facility required precise geometric adjustment at its building connections. Terrestrial laser scanning was used to capture the external building geometry, crane structure and rails in multiple loading positions, creating a measurable point cloud for distance and angular deviation analysis.

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.

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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.

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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?

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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.

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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:

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