Transparency in LiDAR 3D Scanning & Defensible Data: Why Quality Reports Matter
- Daniel Kuzev
- Jan 5
- 5 min read
In this article, we explain why quality reports matter in reality capture, outline DGTL’s approach, and share real-world examples of how they help save time, money, and unnecessary headaches. This context matters more than ever, especially at a time when new laser scanners regularly enter the market, often accompanied by polished videos showcasing beautiful point clouds and promising that capturing accurate and precise data has never been easier.
A building point cloud can appear spectacular yet still pose risks for design, coordination, quantities, or approvals. In construction, confidence does not come from visuals. It comes from traceability: how the data was captured, how it was controlled, and how quality was verified.
For this exact reason, we believe the only way moving forward is to provide a Quality Report on every single 3D scanning project by default. It documents the methodology, summarizes the results, and includes statistical validation, allowing architects, engineers, and project teams to use the deliverables with confidence for 2D drawings, 3D models, and reliable measurements. At the end of the day, our motto is Data-Driven-Design, and we stand by it.
The problem
LiDAR 3D scanning is powerful, but there is a quiet problem in the industry: deliverables without real assurance. Without documented quality, the people who actually need to use the data are left guessing:
What coordinate system is this really in?
How good is the georeferencing?
Was drift addressed? If so, how and to what extent?
What level of accuracy can we safely rely on?
Is 98% accuracy enough? How about 99%?
When those answers are missing, teams tend to slow down, question the data, set it aside, or decide to start over with a new scanning mandate that meets their needs. That hesitation costs time, money, and momentum, usually when a project can least afford it.
What a Quality Report Actually Is
A Quality Report is not marketing, and it is not a gimmick. It exists for transparency, plain and simple. It demonstrates how the data was captured, controlled, and how different datasets were integrated, as well as the consistency of the final point cloud.
Yes, producing this kind of report takes more effort. But skipping it is how uncertainty creeps into a project and quietly becomes expensive, especially on complex buildings, renovations, and multi-storey assets.
DGTL's approach
In real projects, one sensor rarely covers everything. This is why our team believes in a hybrid approach: combining complementary technologies such as terrestrial LiDAR, mobile LiDAR (SLAM), drone capture, and more, and integrating them into a single controlled framework.
Before getting into the technical details, one thing is worth stating clearly. From our perspective, technology does not replace professional judgment. It amplifies it. The real strength of our workflow comes from the marriage of architecture and land surveying under DGTL’s roof.
Architectural expertise ensures completeness. It defines what actually needs to be captured for drawings, models, and scope decisions, not just what is easy to scan. Survey discipline protects precision. It keeps geometry under control, constrains drift, and makes sure the dataset remains defensible when it is time to rely on it.
Below are examples and explanations taken directly from our Quality Reports. They illustrate how our workflow is structured to provide the highest possible level of assurance and transparency, from data capture all the way to final validation.
Georeferencing: one real coordinate system
Georeferencing ties the project to a real, shared coordinate system, so distances, areas, and elevations mean the same thing everywhere instead of drifting into relative guesswork.
In practice, this also means not relying on a single measurement. Control points are observed multiple times and averaged, not to make the data look better, but to reduce random error before those points become the reference for everything that follows.
A primary control network: the project’s geometric backbone
A primary control network establishes a stable set of control points measured with high-precision surveying instruments. This becomes our geometric "truth", the framework everything else must agree with.
Each control point is measured repeatedly, and those observations are averaged and checked against one another. Outliers are questioned, not ignored. This process gives us confidence not just in a point’s position, but in the reliability of the network as a whole.
This matters because GNSS can naturally carry higher tolerances (often centimetre-level), while a well-built control network prioritizes millimetric relative precision across the project geometry.
Terrestrial LiDAR: precision and interior coordinate transfer
Terrestrial LiDAR produces dense, high-precision point clouds and can transfer coordinates from exterior control into interior spaces where GNSS or total stations cannot be used. This makes it a reliable bridge for building interiors.
Mobile LiDAR (SLAM): speed, with drift constrained
SLAM capture is ideal for large interiors and complex corridors, but it can drift over distance. In our approach, mobile datasets are attached back to the control network using reference points. This limits drift and ensures consistent integration.
Drone capture: coverage that is validated, not assumed
Drone datasets can efficiently capture facades and roofs, but they still must be aligned and verified. Quality control can combine control points and direct integration of LiDAR point clouds as a geometric reference, so alignment with other datasets is verified.
Case Studies
Case Study # 1 - "We already have he plans"
"We already have the plans" is something we hear regularly, and it is often true. The real question is whether those plans come with traceability you can trust.
A client contacted us to digitize and prepare plans (including rentable areas) for two high-rise buildings. Two years earlier, they had mandated an architect to prepare plans for one of those towers. The deliverable looked complete at first glance, but it came as PDF files only. We knew something was off when the architect refused to provide the CAD drawings or any information about how the survey had been conducted, and could not explain how the building had been measured.
Without that traceability, our own architects could not responsibly stand behind the geometry. Some elements in the drawings also showed signs of simplification, such as walls drawn with the same thickness throughout, which can be a red flag when a complex building is reduced to assumptions. In the end, our architects refused to sign off on those drawings and refused to use them for area calculations, because doing so would have transferred risk to the client.
A Quality Report would have made the situation clearer from the start. It would document the methodology, the controls used, the expected accuracy, and the known limitations. Even if the conclusion is that a re-scan is needed, the decision becomes informed, transparent, and defensible, rather than relying on expensive guesswork.
Case Study # 2 - Proactive School Boards
Across Canada, and especially in Quebec, public school boards sometimes digitize schools a year or more before the start of architectural work. The intent is smart. They want to be proactive, reduce site visits, and give future teams a head start with a point cloud that can support planning, budgeting, and early coordination.
The problem is what happens next when that point cloud arrives without documented assurance of quality. Architects and engineers receive a dataset with no clear answer to basic questions about control, drift, completeness, and what tolerances can reasonably be expected for plans and measurements. In that context, many design teams will do the responsible thing and refuse to rely on it. The school board then ends up paying twice, once for the initial scan and again for a replacement that comes with the traceability they need.
A Quality Report makes the dataset adoptable. It explains what was done, how it was controlled, what was verified, and what the data is fit for. It also documents known limitations so everyone can plan around them. This clarity protects public budgets, reduces re-surveys, and keeps projects moving forward instead of restarting from zero.
In conclusion, transparency accelerates projects
In 3D LiDAR scanning, the point cloud is only part of the deliverable. The Quality Report is the proof that the scan is controlled, coherent, and fit for purpose, so your team can move forward confidently.
If you are planning a LiDAR scan for a building, book a call, and we will help you define scope, tolerances, and deliverables for your project.
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