Lab notice Q3 point-cloud registration benchmark published — 41 control networks, full residual tables included. Read the benchmark
Abstract three-dimensional geometry rendered from captured point data

Reality capture laboratory — Amsterdam

Measured reality.
Published residuals.

We scan, register and rebuild physical assets as measurable digital geometry — and we publish the error tables behind every accuracy figure we quote, including the captures that failed tolerance.

Working with 40+ infrastructure, heritage and manufacturing teams across Europe

Rijnmond HavenwerkenKasteel FoundationNordvest Rail Helix PrecisionAtelier Meridiaan

Capture disciplines

All capabilities

Six practice areas, each with its own published procedure and its own stated uncertainty budget. We will tell you which one your problem needs, including when the answer is that you do not need us.

Sub-millimetre where it is real.

Instrument specifications describe laboratory conditions. Field accuracy depends on control network geometry, surface behaviour and registration method. We quote the number our own control data supports on your site type, not the number on the brochure.

1.8 mmMedian residual at 20m
41Control networks published
6Failed cases documented

Provenance travels with the model.

Every delivered dataset carries its capture date, instrument, operator, control coordinates and processing chain. Five years from now, whoever inherits the asset can tell what the geometry was actually measured from — which is the entire point of a twin.

100%Deliveries with provenance record
24 moRaw scan retention as standard

Recent publications

Full research index
Benchmark

Registration residuals across 41 control networks

Cloud-to-cloud registration compared against total station control on 41 sites. Median residual 1.8mm at 20m, with the full per-site table and the six cases that failed tolerance.

VR-2026-03 · March 2026 · 38 pages · dataset included

Method note

Photogrammetric scale drift in long corridor captures

Why unbraced corridor runs over 60m accumulate scale error, how much, and the two control placements that remove it.

VR-2026-02 · February 2026 · 14 pages

Benchmark

Sensor comparison: phase-shift versus time-of-flight on wet surfaces

Reflective and wet surfaces degrade both sensor families, but not equally. Measured degradation curves for four instrument classes.

VR-2025-11 · November 2025 · 26 pages · dataset included

Fits the stack you already run

Deliverables are handed over in the formats your engineers open on a Monday morning, not in a proprietary viewer that expires.

01 — POINT DATA

Registered clouds

E57, LAS/LAZ and RCP, with the registration report and control residuals attached.

02 — GEOMETRY

Meshes and solids

OBJ, FBX, glTF and STEP where a solid model is the deliverable rather than a surface.

03 — ASSET MODELS

BIM and twin handover

IFC with a documented level of information need, plus the provenance record as structured metadata.

Hanneke Bos Asset manager, Nordvest Rail
“They were the first supplier who told us a tolerance we had written into the brief was not achievable with any instrument, and then showed us the measurements proving it. We rewrote the brief.”

Start with a specification, not a quote

Tell us the asset, the decision the data has to support and the tolerance you believe you need. We will send back a capture specification stating what is achievable, what it costs, and where the uncertainty sits — before anyone signs anything.

Open a commercial enquiry