Metrology · Calibration · Statistics
Accuracy metrology: decomposing a scanner's error budget
Evaluation instruments and statistical analysis to answer, with evidence, how accurate a 3D scanner actually is — and why.
Challenge
A scanner's headline accuracy number hides a sum of mechanisms: sensor noise, calibration bias, registration drift, fusion artifacts. Improving any of them first requires instruments that can tell them apart against real ground truth.
Contribution
Built evaluation instruments in the style of ISO 20896, the accuracy standard for dental scanners, and decomposed the error budget over a 17-scan corpus against industrial ground truth. Isolated a systematic −0.2% lateral scale error (present in 17 of 17 scans, p ≈ 3×10⁻⁵ over 86,000 edge measurements), traced it to the dot pitch of the calibration target — the spacing the model assumes rather than measures — and showed that no downstream correction is well-founded until that pitch is measured directly: the fixes that helped one metric only traded it against another. Also characterized a ±6 µm periodic error in the calibration stage's leadscrew, which the standard sweep could not have resolved: a periodic signal can only be seen if you sample it more than twice per cycle — its Nyquist rate — and the sweep stepped more coarsely than that, folding the ripple into what read as noise.
Outcome
An error budget decomposed into traceable physical mechanisms, each claim carried by measurement — including candidate corrections that were built, measured, and deliberately withheld where a confound remained unresolved.
Client work is described at a technical, client-agnostic level.