The open, community-built database of factory car paint thickness, per make, model, generation and panel.
Plus a repaint and body-filler checker that works offline, right in the garage.

Deutsch · How to measure · How the checker decides · Contributing

_{▶ Watch the full 43-second video (with sound)}

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Why?

Every detailer with a paint gauge asks the same two questions:

1. How much can I safely polish? For that you need to know how much paint the car had from the factory.
2. Has this panel been repainted, or is there filler underneath? Used-car buyers ask this too.

The reference numbers are scattered across forum threads, or locked in closed charts with one rough value per model. Nobody publishes them per panel, as open data, with real measurements behind them. OpenPaintDepth does, and anyone can help build it.

What you can do with it

Check a car Tap a panel on the car drawing and type what your gauge shows. Each panel turns blue, green, yellow, orange or red (thin → original → check closer → repainted → filler), the same scale professional gauges use. Press Enter to walk around the car.
Understand the result A plain-language verdict with a gauge, the panels that stand out, and what each finding means, for example "there is very likely body filler under the paint, do not machine-polish hard here."
Print a report A clean one-page A4 inspection report with the car drawing, every reading, notes and a signature line. Hand it to your customer, or take it to the used-car dealer. Save it as a PDF from the print dialog.
Compare with the database Median and normal range for every panel of every model the community has measured, on one shared scale.

Also: µm or mils, English and German, light and dark mode, and it installs like an app on your phone (open the site, then "Add to Home Screen") and keeps working without signal. Nothing you type leaves your device unless you decide to share it.

Share your measurements, no tech skills needed

The database only grows if people share what they measure. It takes about two minutes:

1. Check a car in the app as usual.
2. Open "Add this car to the open database", pick the make, type the model and year. Everything else is optional.
3. Press "Send via GitHub". A new tab opens with everything already filled in. Click "Create". Done.

A bot checks the numbers automatically, and once a maintainer approves, your car becomes part of the reference for everyone.

What is GitHub, and why do I need an account?

GitHub is the free website where OpenPaintDepth and all of its data are stored openly, a bit like a public archive that anyone can read and copy. Sending through GitHub keeps the data open and lets everyone see where it came from. A free account takes about a minute: github.com/signup.

What exactly is shared?

Only the car model, year, the paint details you choose to enter, and the readings. Never a VIN, a plate number, your name or your location. Your GitHub username is shown as the contributor.

I don't know the car's history. Is my measurement still useful?

Yes. Choose "I don't know the car's history". Obvious repaints are filtered out of the reference automatically. If you know a panel was repainted, tick it, so it isn't counted as factory paint.

How do I measure properly?

Clean, dry panels, about three spots per panel, away from edges. The full guide is in docs/measuring.md.

Status: freshly launched. The database ships with a few clearly labelled example records so you can see how it works. They never mix with real statistics. Your car could be the first real measurement.

Roadmap

• First 100 real cars
• Bluetooth import from common paint gauges
• Your own workshop logo on the printed report
• Paint-code level statistics (some colours are thicker than others)
• More languages: FR, ES, IT, PL, NL… (help wanted)
• Versioned dataset releases for researchers

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For developers

The data is plain YAML in this repository, validated by a JSON Schema in CI. The app is Vite + TypeScript with no runtime framework.

npm install
npm run dev        # app at http://localhost:5173 (add ?example to load the example car)
npm test           # unit tests
npm run validate   # check all data files
npm run build      # validate + build db.json + type-check + production build

One measured car is one file:

schema: 1
vehicle:
  make: volkswagen
  model: Golf
  generation: Mk7
  year: 2016
  paint_type: metallic
condition: factory          # factory = known original paint, unknown = no known history
repaired_panels: [door_rr]  # left out of the reference statistics
readings:  # µm, one entry per measured spot
  hood: [118, 121, 116]
  roof: [112, 115, 110]
  door_rr: [214, 236, 221]

data/makes.yaml              canonical list of makes
data/measurements/**.yaml    one file per measured car  ← the open data
schema/                      JSON Schema for a measurement
src/core/                    shared logic: panels, statistics, checker rules, YAML writer
scripts/                     validate, build-db (→ web/public/db.json), issue intake bot
web/                         the app
.github/workflows/           CI, GitHub Pages deploy, issue → pull request intake

Submissions from the app arrive as GitHub issues. .github/workflows/intake.yml validates them and opens a pull request automatically. See CONTRIBUTING.md for the details.

License

• Code: MIT
• Data (data/): Open Database License (ODbL) 1.0. Use it anywhere, including commercially, but credit OpenPaintDepth and keep derived databases open.