Exploring AI’s role in scan-to-CAD

Posted on February 23, 2026 at February 23, 2026 by DEVELOP3D

5 0

Data capture technology has made some spectacular advances in recent years, but the process of putting scans to work in CAD has always been a stumbling block. DEVELOP3D spoke to four industry insiders about what changes AI will bring to 3D scanning and what this means for product design and engineering.

Art Yukhin
President & CEO/Artec3D

The digital revolution is already well underway and 3D modelling is diversifying. Users are no longer tied to arbitrary file types. They can quickly digitise entire scenes by combining different data sources. If someone had told me this would be possible 10 years ago, I wouldn’t have believed it!

Now, the challenge is finding a way of extracting information from these 3D models.

Arguably, AI is the biggest buzzword in the industry right now, but it can play a very real role in reverse engineering and inspection processes. Let’s say you’re repairing an engine. With our metrology-grade 3D scanners, you could digitise the entire block, then manually compare scans to CAD drawings. This would allow you to see if a plane is flat enough, or if an angle falls within predefined tolerances.

AI is arguably the biggest buzzword in the industry right now, but the technology can play a very real role in reverse engineering and inspection processes

But imagine if there’s tons of data, like in a production scenario. This is where automation is key. We recently launched Workflows in Artec Studio, our AI-powered software, which automatically fuses captured data (this could be photos, videos or various types of 3D scan) into a watertight model, ready for analysis. It even features scripting, so users can create an end-to-end workflow, go completely hands-off and carry out other tasks. This is only getting easier for engineers to accomplish.

With this approach, you no longer need the hands of a technician. You don’t need a PhD. And you don’t need to spend a lot of time. You can also feed big data directly to AI and extract the information you need. That’s what our team is working on. There are different names for it. We call it ‘Scan-to-CAD’.

Artec Studio already has all the essentials for autosurfacing, surface thickening, and key feature extraction. But now we’re going further.

Over the next decade, I anticipate this becoming a trend. We’re working on streamlining the process and users can already see the benefits in our products. As such, AI isn’t just a buzzword for us. We’re harnessing the technology to deliver actionable insights. I’m excited to see how AI changes the game here in the near future.

Adam Stanley
Managing Director/T3DMC

AI will make 3D scanning far more usable for designers and engineers by removing the interpretation bottleneck that sits between capturing data and creating value from it.

For the last few years, scanning hardware has leapt ahead. We can generate clean, dense 3D data faster than ever. The real challenge now is what happens after the file is created. Today, someone with specialist expertise must still spend hours turning point clouds and meshes into something practical — a CAD model, an inspection result, or a decision the business can act on.

AI can learn to recognise geometry, segment surfaces, fit features automatically and propose a model that an engineer can refine

This is exactly where AI will change the game. Most design and engineering workflows don’t need an artistic surface model; they need intent. In CAD terms, that means cylinders, planes, circles, holes, edges, and functional features that can be edited parametrically. Because this work already happens in a digital environment, AI can learn to recognise geometry, segment surfaces, fit features automatically and propose a structured model that an engineer can refine, rather than build from scratch. The result is faster reverse engineering, faster model creation and far fewer manual steps.

The same shift applies to inspection. AI will increasingly interpret drawing requirements, identify what needs to be checked, extract measurements from scan data and report pass/fail against defined tolerances (including GD&T characteristics), with exceptions highlighted for review.

That turns inspection from a labour-heavy programming exercise into an exception-driven workflow, where engineers focus on risk and root cause, instead of repetitive set-up.

However, there’s a nonnegotiable caveat: AI will only be as reliable as the data it is fed. ‘Data in, data out’ isn’t a slogan; it’s an engineering constraint. If scan data is too noisy, incomplete or captured without good process discipline, the AI will either fail or, worse, produce plausible-looking outputs that aren’t trustworthy.

The organisations that benefit most will be those that pair AI automation with robust measurement methods, consistent workflows and validation on representative parts.

In short, AI isn’t going to be about making 3D scanning faster. It will be about making scan data usable, translating raw capture into design intent and measurable engineering decisions at production speed.

James Earl
Managing Director/OR3D

Modern scanners can capture highly detailed geometry quickly, but for many designers and engineers the challenge begins once the data is captured. Raw meshes and point clouds are information rich yet difficult to translate into usable CAD. Artificial intelligence is increasingly helping to close this gap, but its effectiveness depends heavily on intent.

A critical first step in any reverse engineering project is understanding the desired end deliverable. Is the goal to create a design-intent CAD model, where wear, distortion and manufacturing tolerances are interpreted and potentially corrected? Or is an exact CAD representation of the scanned part required, accurately reflecting the as-built or as-worn condition? This distinction fundamentally shapes how scan data should be processed.

Without this clarity, even the most advanced AI tools can produce technically impressive but practically unsuitable results. Nuance is essential whenever CAD models are derived from scan data alone. AI adds value by accelerating and structuring the interpretation of scan data. Tasks such as mesh clean-up region segmentation and feature recognition can now be automated to a high degree. More importantly, AI can assist in identifying underlying geometric intent, enabling scan data to be transformed into editable, parametric CAD rather than remaining a static reference.

A strong example of this is Geomagic’s Design X software, where automated workflows are embedded directly into the reverse engineering process. Its wizard functionality guides users from raw scan data through to CAD, analysing geometry and recommending appropriate feature-based workflows. Planes, cylinders, holes and blends can be recognised automatically, while still allowing engineers to decide whether features should represent nominal design intent or remain true to the scanned condition.

This guided approach lowers the barrier to entry for non-specialists, while giving experienced users the control needed to manage tolerances, symmetry, datum strategy and downstream manufacturability. The result is CAD that is not only accurate, but usable across design, simulation and production.

At OR3D, working daily with complex scan data, we see AI as an enabler rather than a replacement for engineering judgement. When AI tools are combined with clear intent, quality data and domain expertise, 3D scanning becomes a practical, reliable part of everyday engineering workflows rather than a specialist bottleneck.

Rachel Zucker
Head of AI/Backflip

3D scanning is nearing a massive leap in accessibility, driven by new AI tools that are finally addressing scanning’s key weakness. The technology has long been held back by the incompatibility of 3D scan data with CAD software, but new AI models like ours can now quickly rebuild a scanned object as a native, parametric model in CAD.

There’s fantastic 3D scanner hardware on the market that gets better every year and it’s never been easier to create a high-quality 3D point cloud or mesh from a physical object. But engineering CAD software wasn’t designed to edit meshes, so users typically get stuck rebuilding a 3D scan manually before they can do anything with it. Backflip accelerates this process and reconstructs the scanned geometry in CAD automatically.

Not just any AI model can handle this task, though. Our team has had to develop specialised AI models with innate 3D comprehension that treat 3D scan reconstruction as a planning and reasoning problem. A CAD model is not just a shape. It’s a sequence of decisions. Given a noisy, partial observation (a scan), the system must infer the most likely sequence of CAD operations (sketches, extrusions, fillets, etcetera) that a proficient CAD engineer would have chosen to produce that geometry. This is a combinatorially hard, ambiguous task and full of trade-off s.

Humans do it via intuition driven by experience. We’re building AI that’s fluent in 3D geometry and can navigate those same challenges. Currently, Backflip’s AI models can take 3D scanned geometry and reconstruct editable, parametric CAD with a feature tree, letting engineers immediately modify or remanufacture a part design.

If you give three different engineers a scan, you tend to get three unique paths to modelling the same part

Over time, these tools will evolve into something even more powerful: systems that understand design patterns, functional intent and manufacturing constraints, and which can bake that knowledge into the generated sequence of CAD operations.

What’s next? Modelling strategy personas. If you give three different engineers a 3D scan, you tend to get three unique paths to modelling the same part. Everyone designs in CAD differently. What if our tools can not only build a part in your CAD, but model it the same way you would, for a specific manufacturing process? That’s when we truly supercharge your workflow.