thicken

thicken turns photos of a real object into a GLB model at its real size and its metadata as JSON, read off the object by vision models and looked up in public catalogs. What it knows about each kind of object lives in a profile: a few come with thicken, and you can add your own.

The other half: its metadata. Part of the first one's, a graded Giannis Antetokounmpo card:

{
  "profile": "slab",
  "holder": "cgc",
  "grader": "cgc",
  "cert": "6072896005",
  "widthMm": 80.5, "heightMm": 135, "depthMm": 7.5,
  "read": {
    "grade": "9", "gradeName": "MINT",
    "autoGrade": "10", "autoGradeName": "AUTO",
    "signed": "G. ANTETOKOUNMPO, 8/2/25",
    "name": "Giannis Antetokounmpo", "year": "2024-25", "maker": "Panini",
    "set": "Court Kings Basketball", "number": "#5", "variant": "Blank Slate"
  },
  "readBy": { "grade": "agreed", "autoGrade": "agreed", "set": "disputed" },
  "inside": {
    "card": { "widthMm": 63.5, "heightMm": 88.9, "corners": "square", "foil": "none" },
    "label": { "widthMm": 66.4, "heightMm": 26.9 }
  }
}

The grader and certificate number come from the label's QR code; the vision models read the rest off the slab. What each profile records is in the table of profiles, and every field is described in USAGE.md.

Contents

• Try it · From an AI agent · How it works · The thicken object · What it makes

• In detail (USAGE.md): batch · photos · vision · video · training mode · review · the sidecar · previews · inputs · sizes

• What leaves your machine · Adding a profile · Security · Built on · Development · License

Related MCP server: 3D CAD BasePlate Generator MCP

Try it

npm install -g thicken                      # Node 22.18 or later; or npx thicken …
thicken photo.jpg                           # it works out what it is: a code on it, or the vision models
thicken book cover.heic back.heic spine.heic
thicken --isbn 9780061120084                # no photo: the cover and size from Open Library
thicken slab front.jpg back.jpg
thicken card front.jpg back.jpg --pt 130    # a thick patch card
thicken badge badge.png --width 50          # cut-out artwork, extruded
thicken lettering thicken                   # the logo
thicken batch ~/Pictures/books --preview    # a model per photo, PDF, video or folder

Each run writes <name>.glb and <name>.json beside the first picture, and with --preview a picture of both sides. Photos straight on and cropped work as they are; uncropped photos, angled shots and video need a vision roster, any OpenAI-compatible endpoint (Vision). As a library: import { thicken } from 'thicken', then thicken(files, options) gives the GLB's bytes and the sidecar.

From an AI agent

thicken is an MCP server too (thicken mcp), with an Agent Skill that tells the agent when and how to use it: ask for a model of the book on your desk, and the agent makes it. In Claude Code:

/plugin marketplace add bikemeardsley/thicken
/plugin install thicken@thicken

Or for any MCP client, npx -y thicken mcp on stdio. Codex, Copilot, Cursor, Antigravity, Gemini CLI and the rest are in USAGE.md.

How it works

What thicken knows about a kind of object lives in its profile: how to recognize it, its faces, what to read off it, and how to build it at its real size. A code on the object picks the profile (an ISBN barcode, a grader's QR code), or the vision models say which it is. thicken is not a photogrammetry tool: everything it handles is close to a box or a flat outline, so it builds the geometry from known sizes and maps your photos onto it.

The thicken object

What thicken makes is more than a 3D shape. Each run writes a thicken object: the model (a GLB at the object's real size, in millimeters) and, beside it, its metadata (the JSON sidecar), which knows what the object is, in its profile's own terms. For the profiles that come with thicken, for example:

• A book: its ISBN, its catalog record (title, authors, publishers, dates, subjects, Dewey and Library of Congress classes, pages), and how sure the match is when it was found by title.

• A graded slab: its grader, cert number and verification link, its holder, and the card and label inside it, each sized and placed. With vision, the label read: the card's grade (its number and words, "9 MINT", any qualifier and subgrades), an autograph's own grade when the label grades one apart from the card ("AUTO 10"), who signed it and when, and the card as the grader names it.

• A raw card: its size, its thickness in points for a thick card, its corners and its foil. With vision, what's printed on it: player or character, team, year, maker, set, number, parallel.

• A box: with vision, its name, brand, edition, rating and year.

• A badge: with vision, what it names, who issued it, its level, subject, date, any credential number, and every other word on it.

• Every object: what the vision models read off it, and how sure; which faces are your photos and which are stand-ins, how each fact was found, what is still worth photographing, and what each vision model answered.

An app shows the model and reads the sidecar: a library shelf of your books, a vault of your slabs. The fields are under What the sidecar records.

What it makes

thicken makes the 3D object; what it knows about kinds of object lives in profiles, one file each in src/profiles. Anyone can add one (CONTRIBUTING.md). Every profile there today:

What each one records in its sidecar, beside what every model has (its size and where it came from, its faces and pictures, its codes, what the vision models read and how sure, what's worth a second look: all in USAGE.md):

Name the profile, or leave it out and thicken picks one:

• An ISBN barcode on any picture, or --isbn, means a book.

• A QR code pointing at a grader's site (psacard.com, beckett.com, cgccards.com, gosgc.com, taggrading.com) means a slab, and says whose. The sidecar keeps the grader, the cert number from the link, and the link itself (verifyUrl).

• So does an older label's Code 128 cert barcode, but only on a photo shaped like a holder. The grader is then the one the vision models read off the label; with no vision, a PSA-size holder is assumed, with a warning.

• Artwork with transparency around a shape that isn't just a rectangle means a badge.

• Otherwise, with vision, the models are shown the profiles and say which it is, and roughly how big: two agreeing settle it, as with faces, and box takes anything box-shaped the others don't cover.

• Anything still unknown is refused with a request to name the profile. Other barcodes don't count: books carry UPCs, QR codes and library stickers.

The sidecar's identifiedBy.profile says which way it was chosen: given, recognized (codes or the artwork), or vision.

Adding a profile is one file in src/profiles, found by itself, nothing else to change: its name; a few words on what it is and on each of its faces, which is all the vision models are told; what to read off it (more is better: the sidecar is for apps to take what they want from); optionally how its codes or pictures give it away unaided; and how to build it from the photos placed on its faces. Generic profiles are named for the shape they build (box, and later perhaps flat or cylinder), not "unknown": a shape thicken can't build is refused rather than forced into one it can. Contributions welcome: CONTRIBUTING.md says how.

Pictures are placed on faces by what they show (see Photos). With no front, Open Library's cover stands in, marked for review, so one photo of the back is enough to start a book.

What leaves your machine

With THICKEN_VISION_URL set, the photos themselves (re-encoded, so without their EXIF) go to that host. A LAN server you control still forwards each photo to the model's provider; a hosted endpoint sees every photo. The only other calls out are a book's catalog lookups (Open Library, the Library of Congress, Google Books), which send its ISBN, or the title and author the models read off it; never a photo.

Nothing else is sent anywhere, and nothing is fetched at run time but those lookups: previews draw with model-viewer served from your own disk.

Adding a profile

A profile is one file, found by itself: what the object is, its faces, what to read off it, and how to build it. Make your own while you use thicken, or improve one of its own, and offer it back:

thicken profile new vinyl          # from the template, in ~/.thicken/profiles: used at once
thicken profile check vinyl        # what thicken's CI will check
thicken profile contribute vinyl   # forks, branches, checks and opens the pull request

The template, templates/profile.ts, works as it is (a box at the size you give) and says on every field what goes there, the records it writes included. CONTRIBUTING.md has the rest. The table above is made from the profiles themselves.

Security

Report a vulnerability privately, as SECURITY.md says. It also lists what thicken touches: files, the network, and the programs it runs.

Built on

For refractor and holo effects in a viewer, simeydotme/pokemon-cards-css is a reference only. It is GPL-3.0, so read it and don't copy it.

Development

TypeScript on Node 22.18+, with sharp for images. From a clone it runs directly (Node strips the types). The npm package is JavaScript built into dist (npm run build, run by npm pack and npm publish), because Node won't strip types inside node_modules. Every push installs the packed package on Windows, macOS and Linux and runs it.

From a clone: npm install, then node src/cli.ts … in place of thicken …; npm test, npm run check, and npm run readme after changing a profile.

License

MIT. See LICENSE.

The lettering font, Titan One, is under the SIL Open Font License 1.1 (fonts/OFL.txt).