← All projectsGitHub
Sensing, imaging and 3D

How accurate must a scan be to rebuild a part as parametric CAD?

research/sensing-scan2cad

How accurate must a scan be to rebuild a part as parametric CAD? On synthetic plate scans, RANSAC plane and circle fits give all five dimensions within 0.2 mm for noise up to 0.2 mm, but at 0.5 mm noise the error is a bias (thickness −0.20 mm) that more points do not remove, and a 0.5% scale error alone moves an 80 mm length by 0.40 mm. Synthetic, one shape.

12 tests

What it found

Synthetic point-cloud scans of an 80×40×10 mm plate with two 8 mm holes, fitted by RANSAC planes and circle fits. With scan noise of 0.2 mm or less every scan gives all five dimensions within 0.2 mm at 2 000 points or more (worst RMS 0.09 mm). At 0.5 mm noise only 0–50% of scans are usable at any point count because the error is a bias: thickness reads 0.20 mm low and the bias grows with the inlier band (−0.07 to −0.38 mm for 1.5σ to 4σ). A scale error of 0.5% from a reconstruction with no scale reference already moves an 80 mm length by 0.40 mm. Stylised: one shape, assumed Gaussian noise, pre-registered scan, generated CadQuery script not run.

A figure from its own output

Figure produced by research/sensing-scan2cad: usable_vs_points
sensing-scan2cad/experiments/usable_vs_points.svg, written by the project's own experiment code.

Paper, code and results

More in Sensing, imaging and 3D

All 31 Sensing, imaging and 3D projects →

Other research topics

Proper scoring & elicitation28Effort & peer prediction9Verification games & slashing19Detection & tolerances14Markets as routers13Prediction-market design9Paying contributors8Decentralised training systems23Digital twins & sim-to-real164LLM chip design loops40Robot manufacturing loops53Ambient AI assistant46Self-replicating manufacturing34Simulation-first design64Engineered clothing45Task-specific AI accelerators40Algorithmic chip-design loops42Open-source purpose-built robots44The software-engineering loop43Adversarial human imitation41Open-source residential construction41Autonomous multi-angle capture34Sensing, imaging and 3D31Energy supply and dense energy storage39Robot societies among people31Agentic co-design of low-cost robots27Laundry-hanging robots25Machine learning on qualitative data23Robots on the building site27Civilizations & group psychology1History & human sciences1

Produced with AI assistance (Claude). Results are from the code in this repository and are simulated or stylised unless the abstract says otherwise; nothing here implies affiliation with or endorsement by any lab, researcher or company cited. MIT licensed.