Ifc Lite

What is Ifc Lite about?

Ifc Lite provides a full‑stack solution for handling IFC and related BIM data. It exposes a Rust/WASM core for fast parsing, columnar storage, and geometry processing, and a set of TypeScript packages for rendering (WebGPU), querying, editing, validation, and exporting.

How to use Ifc Lite?

1. Create a starter project

   npx create-ifc-lite my-viewer --template react
   cd my-viewer && npm install && npm run dev
   

2. Add packages to an existing project

   npm install @ifc-lite/parser @ifc-lite/geometry @ifc-lite/renderer
   

3. Parse a file

   import { IfcParser } from '@ifc-lite/parser';
   const parser = new IfcParser();
   const buffer = await fetch('model.ifc').then(r => r.arrayBuffer());
   const store = await parser.parseColumnar(buffer);
   

4. Render with WebGPU

   import { GeometryProcessor } from '@ifc-lite/geometry';
   import { Renderer } from '@ifc-lite/renderer';
   const geometry = new GeometryProcessor();
   const renderer = new Renderer(canvas);
   await Promise.all([geometry.init(), renderer.init()]);
   // feed meshes produced by geometry to renderer
   

5. Query, edit, validate, export – use the dedicated @ifc-lite/query, @ifc-lite/mutations, @ifc-lite/ids, and @ifc-lite/export packages as demonstrated in the README.

Key Features

• Cross‑platform: Browser (WebGPU), Node server, and desktop (Tauri) builds.
• Fast parsing: ~1.2 GB/s tokenization, 5× quicker geometry than competitors.
• Full schema support: IFC2X3, IFC4/IFC4X3, IFC5 (IFCX).
• Columnar data model: Efficient storage, easy DuckDB/Polars querying, Parquet export.
• Rich ecosystem: 25 TypeScript packages + 4 Rust crates covering parser, geometry, renderer, query, mutations, IDS validation, BCF handling, 2‑D drawing, export formats (glTF, STEP, Parquet, IFC5).
• Small bundle: ~260 KB gzipped (parser + geometry + renderer).
• Extensible: Plug into Three.js, Babylon.js, or custom native Rust pipelines.

Use Cases

• Web‑based IFC viewers for architects, engineers, and contractors to inspect models without installing software.
• Server‑side preprocessing of large IFC files (caching, streaming, parallel processing) for BIM collaboration platforms.
• Desktop BIM tools built with Tauri for offline work on massive models (>500 MB).
• Quality assurance by validating models against IDS specifications.
• Data export pipelines converting IFC to glTF for visualization, to Parquet for analytics, or back to STEP for exchange.
• Integration with existing 3D engines (Three.js, Babylon.js) to add BIM support to games, VR/AR apps, or custom simulators.

FAQ

• Does it work on all browsers? Requires WebGPU support (Chrome, Edge, Safari Tech Preview). Fallback to Canvas‑based engines via Three.js/Babylon.js is possible.
• Can I process models larger than 1 GB? Yes, the server/desktop setups handle very large files; the browser version is best for models up to a few hundred MB.
• Is the library free for commercial use? Licensed under MPL‑2.0, which permits commercial use with source‑file level copyleft.
• How do I add BCF issue tracking? Install @ifc-lite/bcf and use the provided client APIs to create, read, and sync BCF topics.
• What language bindings are available? Primary bindings are TypeScript/JavaScript (npm packages) and Rust crates; the WASM module can be called from any language that can load WebAssembly.

---

IFClite

Parse, view, query, edit, and export IFC files in the browser. Rust + WASM core, WebGPU rendering, ~260 KB gzipped, 5× faster geometry than the next best option.

Works with IFC2X3, IFC4 / IFC4X3 and IFC5 (IFCX). Live demo at ifclite.com and more info here: ifclite.dev.

Get Started

npx create-ifc-lite my-viewer --template react
cd my-viewer && npm install && npm run dev

That gets you a working WebGPU IFC viewer with drag-and-drop, hierarchy, properties, and 2D drawings. Other templates: basic, threejs, babylonjs, server, server-native.

To add IFClite to an existing project:

npm install @ifc-lite/parser @ifc-lite/geometry @ifc-lite/renderer

Parse an IFC file

import { IfcParser } from '@ifc-lite/parser';

const parser = new IfcParser();
const buffer = await fetch('model.ifc').then(r => r.arrayBuffer());
const t0 = performance.now();
const store = await parser.parseColumnar(buffer, {
  onProgress: ({ phase, percent }) => console.log(`${phase}: ${percent}%`),
});

console.log(`${store.entityCount} entities, schema ${store.schemaVersion}`);
console.log(`Parsed in ${(performance.now() - t0).toFixed(0)}ms`);

View in 3D

import { IfcParser } from '@ifc-lite/parser';
import { GeometryProcessor } from '@ifc-lite/geometry';
import { Renderer } from '@ifc-lite/renderer';

const parser = new IfcParser();
const geometry = new GeometryProcessor();
const renderer = new Renderer(canvas);

await Promise.all([geometry.init(), renderer.init()]);

const arrayBuffer = await file.arrayBuffer();
const store = await parser.parseColumnar(arrayBuffer);
const meshes = [];
for await (const event of geometry.processAdaptive(new Uint8Array(arrayBuffer))) {
  if (event.type === 'batch') meshes.push(...event.meshes);
}

renderer.loadGeometry(meshes);
renderer.requestRender();

// Pick an entity at (x, y) in canvas pixels
const hit = await renderer.pick(120, 240);
if (hit) console.log(`Picked expressId ${hit.expressId}`);

For Three.js or Babylon.js, parse + extract geometry the same way and feed meshes to your engine. See Three.js integration and Babylon.js integration.

Query entities

import { IfcQuery } from '@ifc-lite/query';

const query = new IfcQuery(store);

// All external load-bearing walls
const walls = query
  .ofType('IfcWall', 'IfcWallStandardCase')
  .whereProperty('Pset_WallCommon', 'IsExternal', '=', true)
  .whereProperty('Pset_WallCommon', 'LoadBearing', '=', true)
  .execute();

console.log(`${walls.length} external load-bearing walls`);

for (const wall of walls) {
  console.log(wall.name, wall.globalId);
}

For more complex queries, use SQL via DuckDB-WASM:

const result = await query.sql(`
  SELECT type, COUNT(*) AS n FROM entities GROUP BY type ORDER BY n DESC LIMIT 10
`);
console.table(result.rows);

Validate against IDS

import { parseIDS, validateIDS, createTranslationService } from '@ifc-lite/ids';

const idsSpec = parseIDS(idsXmlContent);
const translator = createTranslationService('en');
const report = await validateIDS(idsSpec, store, { translator });

for (const spec of report.specificationResults) {
  console.log(`${spec.specificationName}: ${spec.passRate}% passed`);
}

Edit properties (with undo)

import { MutablePropertyView } from '@ifc-lite/mutations';
import { PropertyValueType } from '@ifc-lite/data';

const view = new MutablePropertyView(store.properties, 'my-model');

view.setProperty(
  wallExpressId,
  'Pset_WallCommon',
  'FireRating',
  'REI 120',
  PropertyValueType.Label,
);

console.log(view.getMutations()); // change history for undo / export

Export

import { exportToStep, ParquetExporter, Ifc5Exporter } from '@ifc-lite/export';
import { GeometryProcessor } from '@ifc-lite/geometry';

// IFC STEP — applies any pending mutations
const stepText = exportToStep(store, { schema: 'IFC4', applyMutations: true });

// glTF / GLB, CSV and JSON-LD are assembled in Rust (ifc-lite-export) via the
// GeometryProcessor — the standalone GLTFExporter/CSVExporter/JSONLDExporter
// classes were retired.
const gp = new GeometryProcessor();
await gp.init();
const glb = gp.exportGlbFromMeshes(result.meshes); // Uint8Array (no re-mesh)
const csv = gp.exportCsv(buffer, 'entities', ',', /* includeProperties */ true);
const jsonld = gp.exportJsonld(buffer);

// Parquet — columnar, ~20× smaller than JSON, queryable from DuckDB / Polars
const parquet = await new ParquetExporter().exportEntities(parseResult);

// IFC5 / IFCX — JSON + USD geometry
const ifcx = new Ifc5Exporter(store, meshes).export({ includeGeometry: true });

Choose your setup

Setup	Best for	You get
Browser (WebGPU)	Viewing and inspecting models	Full-featured 3D viewer, runs entirely client-side
Three.js / Babylon.js	Adding IFC support to an existing 3D app	IFC parsing + geometry, rendered by your engine
Server	Teams, large files, repeat access	Rust backend with caching, parallel processing, streaming
Build for Desktop	Your own offline native app, very large files (500 MB+)	Extension points to wrap the packages in Tauri, with an optional native-Rust geometry fast path

Not sure? Start with the browser setup. You can add a server or switch engines later.

Pick your packages

I want to...	Packages
Parse an IFC file	@ifc-lite/parser
View a 3D model (WebGPU)	+ @ifc-lite/geometry + @ifc-lite/renderer
Use Three.js or Babylon.js	+ @ifc-lite/geometry (you handle the rendering)
Query properties and types	+ @ifc-lite/query
Edit properties (with undo)	+ @ifc-lite/mutations
Validate against IDS rules	+ @ifc-lite/ids
Generate 2D drawings	+ @ifc-lite/drawing-2d
Create IFC files from scratch	@ifc-lite/create
Export to glTF / IFC / Parquet	+ @ifc-lite/export
Connect to a server backend	+ @ifc-lite/server-client
BCF issue tracking	+ @ifc-lite/bcf

Full list: API Reference (25 TypeScript packages, 4 Rust crates).

Performance

• First triangles: 200–500ms for a typical 50 MB model in the browser.
• Geometry processing: up to 5× faster than web-ifc on the same hardware.
• Bundle size: ~260 KB gzipped (parser + geometry + renderer).
• Schema coverage: 100% of IFC4 (776 entities) and IFC4X3 (876 entities).
• Parse throughput: ~1,259 MB/s tokenization on a typical M1 / M2 laptop.

See benchmarks for full numbers across model sizes and hardware.

Examples

Ready-to-run projects in examples/:

• Three.js Viewer — IFC viewer using Three.js (WebGL)
• Babylon.js Viewer — IFC viewer using Babylon.js (WebGL)

Documentation

Start here	Quick Start · Installation · Browser Requirements
Guides	Parsing · Geometry · Rendering · Querying · Exporting
BIM features	Federation · BCF · IDS Validation · 2D Drawings · Property Editing
Tutorials	Build a Viewer · Three.js · Babylon.js · Custom Queries
Deep dives	Architecture · Data Flow · Performance
API	TypeScript · Rust · WASM

Contributing

The WASM bundle is built from rust/ on every fresh build, so a Rust toolchain is required. rust-toolchain.toml pins the nightly channel and the wasm32-unknown-unknown target — rustup show (or the contributing setup guide) installs everything needed.

# 1. Rust toolchain (one-time)
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
cargo install wasm-pack   # or: curl https://rustwasm.github.io/wasm-pack/installer/init.sh -sSf | sh

# 2. Clone and build
git clone https://github.com/LTplus-AG/ifc-lite.git
cd ifc-lite
pnpm install && pnpm build && pnpm dev   # opens viewer at localhost:3000

If you need IFC fixtures for tests, benchmarks, or stress tests, fetch them with:

pnpm fixtures           # download every fixture (idempotent, hash-verified)
pnpm fixtures:check     # CI-friendly: exit 1 if anything is missing or stale

The fixtures are stored on a GitHub Release and catalogued in tests/models/manifest.json — see tests/models/README.md for the full design and maintainer workflow.

See the Contributing Guide and Release Process.

Community

• GitHub Discussions — questions, ideas, show-and-tell
• Issues — bug reports and feature requests
• Releases — changelog and version notes

License

MPL-2.0 — use, modify, redistribute. Source files modified under MPL must remain MPL.