ifclite-geom

Native ifc-lite geometry tessellation for Python. It turns an IFC file into per-entity triangle meshes with no Node, no WASM, and no subprocess: the Rust geometry kernel runs directly inside the Python process.

Meshes come back welded, IFC Z-up, in absolute world metres, keyed by IFC STEP id (occurrences only). This is the analysis-ready export, distinct from the render-oriented GLB the viewer uses.

Install

pip install ifclite-geom

Prebuilt wheels ship for CPython 3.9+ on Linux (x86_64, aarch64), macOS (Apple silicon and Intel), and Windows (x64). No Rust toolchain needed.

Quick start

The module is ifclite_geom and exposes three functions, all taking the raw IFC file as bytes. geometry_data_buffers and geometry_data_json return the same geometry and differ only in output format; pass include_directrices=True to include analytic swept-disk paths. entity_data reads attributes and property sets instead, without tessellating.

import ifclite_geom
import numpy as np

with open("model.ifc", "rb") as f:
    ifc_bytes = f.read()

data = ifclite_geom.geometry_data_buffers(ifc_bytes)

print(data["element_count"], "elements")
print("up axis:", data["up_axis"], "| units:", data["units"])
print("rtc offset:", data["rtc_offset"])

for step_id, el in data["elements"].items():
    verts = np.frombuffer(el["vertices"], dtype=np.float64).reshape(-1, 3)
    faces = np.frombuffer(el["faces"],    dtype=np.uint32 ).reshape(-1, 3)
    print(step_id, el["ifc_type"], el["global_id"], verts.shape, faces.shape)

Prefer no numpy dependency? Use the JSON variant, which returns the same data as arrays of numbers:

import ifclite_geom, json

doc = json.loads(ifclite_geom.geometry_data_json(ifc_bytes))
first = next(iter(doc["elements"].values()))
print(first["ifc_type"], first["vertices"][0])  # [x, y, z] in metres

API

geometry_data_buffers(ifc_bytes: bytes, quality: str | None = None, ids: set[int] | None = None, *, include_directrices: bool = False) -> dict

The fast path. Vertices and faces come back as raw little-endian byte buffers so you can hand them straight to numpy.frombuffer with zero parsing.

{
  "up_axis": "Z",            # always Z (IFC native)
  "units": "m",              # always metres
  "rtc_offset": [x, y, z],   # geo-reference offset already folded into vertices
  "element_count": 1234,
  "elements": {
    <step_id:int>: {
      "ifc_type":  "IfcWall",
      "global_id": "3vB2...",   # may be None
      "name":      "Basic Wall:...",  # may be None
      "color":     [r, g, b, a],      # 0..1
      "vertices":  <bytes>,           # f64 little-endian, xyz triplets
      "faces":     <bytes>,           # u32 little-endian, triangle indices
    },
    ...
  }
}

Decode the buffers with:

verts = np.frombuffer(el["vertices"], dtype=np.float64).reshape(-1, 3)  # (V, 3)
faces = np.frombuffer(el["faces"],    dtype=np.uint32 ).reshape(-1, 3)  # (F, 3)

geometry_data_json(ifc_bytes: bytes, quality: str | None = None, ids: set[int] | None = None, *, include_directrices: bool = False) -> str

The same geometry as a readable ifc-lite-geometry-data JSON document (a string; call json.loads on it). Vertices are [x, y, z] arrays and faces are [a, b, c] index arrays, so no numpy is required. Each element also carries global_id and name when the source entity has them.

Analytic swept-disk paths

Pass include_directrices=True to either geometry function. The result adds swept_disks, keyed by occurrence STEP id, and directrix_diagnostics. Each occurrence can have multiple source IfcSweptDiskSolid items. A description preserves solid_id, directrix_id, Radius, InnerRadius, mapping_path, source_modified, status, and an ordered Directrix of typed line and circular-arc segments. Coordinates are in absolute IFC Z-up world metres, matching mesh vertices. For a complete description, Radius and InnerRadius are the effective world radii in metres. For an unsupported transform, they retain the authored radii converted to metres; the status indicates that no world circular radius is available. Angle values are in radians. The buffer path uses integer keys; the JSON path uses string object keys.

data = ifclite_geom.geometry_data_buffers(ifc_bytes, include_directrices=True)
for step_id, sweeps in data["swept_disks"].items():
    for sweep in sweeps:
        for segment in sweep["Directrix"]:
            if segment["type"] == "line":
                print(step_id, segment["start"], segment["end"])
            else:  # circular arc
                print(step_id, segment["center"], segment["radius"])

An arc also carries normal, x_axis, start_angle, and sweep_angle to define its orientation and travel. status is {"type": "complete"} or {"type": "unsupported", "reason": ...}; unsupported paths have no partial segments. source_modified=True means the sweep is a source operand and later booleans may alter the final solid. Inspect that field before using the source path for fabrication. The flag does not describe cuts from external IfcRelVoidsElement openings. Extraction issues appear in directrix_diagnostics. With the flag omitted, both functions keep their existing output shape and skip this extraction.

For a partial composite sweep or a raw-circle sweep with solid-level bounds, the current triangle mesher may cover a different extent (#5566). The analytic Directrix follows the IFC parameters; check this distinction when comparing it to the returned mesh.

Tessellation quality

Both geometry functions take an optional quality label:

It scales the segment count on every curved primitive: swept-disk tubes, cylinders, revolutions, arcs, circular profiles. On curve-heavy elements the effect is large. A single IfcReinforcingBar authored as an IfcSweptDiskSolid over a composite arc tessellates to 1056 triangles at "medium" and 96 at "lowest".

data = ifclite_geom.geometry_data_buffers(ifc_bytes, "lowest")

An unrecognised label raises ValueError rather than silently falling back, so a typo cannot cost you a 10x triangle budget without saying so. This is the same knob the browser build exposes as setTessellationQuality and the server as ?tessellation_quality=; the level is model-wide, not per IFC type.

Filter by IFC STEP id

Both geometry functions accept an optional ids set. Only matching occurrence ids are tessellated, which lets you select products through entity_data first without paying to mesh the rest of the model:

entities = ifclite_geom.entity_data(ifc_bytes)
wall_ids = {
    step_id
    for step_id, row in entities["entities"].items()
    if row["ifc_type"] == "IfcWall"
}
walls = ifclite_geom.geometry_data_buffers(ifc_bytes, ids=wall_ids)

ids=None preserves the unfiltered behaviour. An empty set returns zero elements, and ids not present in the file are ignored. The pipeline still resolves relationship and representation dependencies: for example, selecting a wall keeps its unselected IfcOpeningElement cutters available to the wall's CSG operation without emitting meshes for those openings.

entity_data(ifc_bytes, placements=False, type_properties=True, attributes=True) -> dict

Attributes, property sets and quantity sets. No tessellation runs, so this is cheap compared with the geometry functions.

{
  "length_unit_scale": 0.001,      # file length unit -> metres
  "plane_angle_to_radians": 0.0174,
  "project_id": 42,                # may be None
  "entity_count": 1234,
  "entities": {
    <step_id:int>: {
      "ifc_type":      "IfcWall",
      "global_id":     "3vB2...",       # may be None
      "name":          "WALL 1",        # may be None
      "description":   None,
      "object_type":   None,
      "has_geometry":  True,
      "placement":     None,            # see below
      "property_sets": [
        {"name": "Pset_WallCommon",
         "properties": [{"name": "IsExternal", "value": "True",
                         "value_type": "IFCBOOLEAN"}]},
      ],
      "quantity_sets": [
        {"name": "Qto_WallBaseQuantities",
         "quantities": [{"name": "Length", "value": 3000.0, "kind": "Length"}]},
      ],
      "attributes": [                  # schema-declared entity attributes
        {"name": "PredefinedType", "value": "SOLIDWALL", "value_type": "IFCENUM"},
      ],
    },
    ...
  }
}

entities is keyed by IFC STEP id in file order, the same key geometry_data_buffers uses, so the two join directly. The join is one-way total: every meshed element has a row, but not every row has an element, so drive the loop from elements (or use .get()) rather than the other way round. Besides products with no geometry, an orphan IfcTypeProduct carries has_geometry: True and still never appears in elements, because the geometry functions emit occurrences only.

geom = ifclite_geom.geometry_data_buffers(ifc_bytes)
ents = ifclite_geom.entity_data(ifc_bytes)

for step_id, el in geom["elements"].items():
    row = ents["entities"].get(step_id)
    if row:
        print(el["ifc_type"], row["name"], row["property_sets"])

Pass placements=True to also resolve each product's ObjectPlacement into a list of 16 floats: a column-major 4x4, translation in metres at indices 12/13/14. It is off by default because it costs an extra decode per product.

The matrix is in the same absolute IFC world frame as geometry_data_buffers vertices, so the two line up directly. Do not fold rtc_offset into either: the geometry export already adds it back into every vertex, and the placement is never RTC-rebased. On a georeferenced model both are large absolute coordinates, and a product's placement origin lands inside its own mesh bounds.

Units, and two current limits

• Property and quantity values are in the file's own units, unlike geometry, which is always metres. A millimetre model reports a wall length of 3000. Property values are always strings; quantity values are floats.

  Converting is per dimension, not one blanket factor:

  quantity kind	to SI
  Length	value * length_unit_scale
  Area	value * length_unit_scale ** 2
  Volume	value * length_unit_scale ** 3
  Count	unchanged (dimensionless)
  angles (properties)	value * plane_angle_to_radians

  Only the length and plane-angle scales are resolved, so a model that declares an area or volume unit inconsistent with its length unit cannot be reconciled from what is returned here.

• Only IfcPropertySingleValue properties are decoded. Enumerated, list, bounded, table and reference properties are skipped; the pset still appears, with those entries missing.

Entity attributes

Note the two senses of "type" on this page. The section below concerns an IfcTypeObject, the shared definition an occurrence inherits from. This one concerns the IFC entity class (IfcWall, IfcReinforcingBar) and the attributes its schema declares. They are unrelated.

attributes is on by default. These are not property sets and no amount of pset work surfaces them, because they are declared on the entity itself:

row = ents["entities"][step_id]
{a["name"]: a["value"] for a in row["attributes"]}
# A bar with every attribute set:
# {'Tag': 'TAG-1', 'SteelGrade': 'B500B', 'NominalDiameter': '29',
#  'CrossSectionArea': '660', 'BarLength': '500',
#  'PredefinedType': 'NOTDEFINED', 'BarSurface': 'PLAIN'}
#
# A bar leaving most of them `$`, which is the common case:
# {'NominalDiameter': '29', 'CrossSectionArea': '0',
#  'PredefinedType': 'NOTDEFINED'}

Only what the file sets is returned. An attribute left $ is omitted rather than reported empty, so the list is usually shorter than the class declares, and its length varies between two entities of the same class.

Every IFC entity class has its own schema-declared attributes: IfcDoor yields OverallHeight / OverallWidth, and so on, named and ordered as the schema declares them. Entries share the {name, value, value_type} shape of a property, so one code path reads both.

Fields the row already carries (global_id, name, description, object_type) are not repeated, and reference-valued attributes are omitted rather than rendered as a dangling #123. Pass attributes=False to skip.

Type-inherited properties

type_properties is on by default. A type attaches its sets through IfcTypeObject.HasPropertySets and gets no row of its own unless it carries orphan geometry, so without this the properties authoring tools put on types are unreachable. Each occurrence therefore also carries what it inherits through IfcRelDefinesByType, merged per property:

• A type set whose name the occurrence does not use is added whole.
• A type set sharing a name contributes only the properties the occurrence does not already define. On a collision the occurrence wins, and the type-only properties beside it still survive. Replacing the whole set instead would hide them, which is the bug this rule exists to prevent.

quantity_sets inherit on exactly the same terms. A type attaches IfcElementQuantity definitions through the same HasPropertySets attribute, so they arrive by the same route and merge by the same rule: a type quantity set the occurrence does not name is added whole, and a same-named one contributes only the quantities the occurrence does not already define, so the occurrence wins a collision. type_properties governs both lists; there is no separate switch.

# Own sets only, as in 4.3.0. Affects property_sets AND quantity_sets.
ents = ifclite_geom.entity_data(ifc_bytes, type_properties=False)

This mirrors what the browser has done since the same fix landed there, so a property visible in the viewer is now visible here.

Notes

• One mesh per element. Per-material submeshes of an element are merged into a single indexed triangle soup, keyed by its IFC STEP id.
• Coordinates are absolute world metres. The per-element local frame and the model RTC offset are folded back into every vertex. For geo-referenced models rtc_offset is non-zero; subtract it if you want f32-friendly local coordinates.
• Welded and indexed. Coincident corners are merged (1 micron grid), so closed-mesh consumers (volume, watertightness checks) work directly.
• Occurrences only. Type-product / RepresentationMap geometry is not emitted, matching what occurrence-based tessellators produce.
• Errors surface as RuntimeError (pipeline failure) or ValueError (an unrecognised quality label, or JSON serialization failure).

Examples

Runnable scripts live in examples/:

• quickstart_numpy.py - load a file and inspect meshes via numpy.
• dump_json.py - write the JSON document to disk.
• export_obj.py - write every element to a single Wavefront .obj (numpy only, no extra deps).
• schedule_csv.py - join entity_data against geometry_data_buffers and write a quantity schedule to CSV (stdlib only).

License

MPL-2.0. Part of the ifc-lite project.