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Scan to BIM

Scan to BIM, the measured way

Scan to BIM starts with measurement: what was built, where the walls are, how big the rooms are, and how far the building departs from its model. Neurones 3D does that part from the point cloud, offline: dimensioned drawings, rooms with areas and volumes, and deviation against an IFC, so the modelling that follows starts from numbers. It does not write an IFC or a Revit model itself.

By Dr. Florent Poux, 3D Geodata Academy · Updated

A dimensioned floor plan drawn from the point cloud: rooms with areas, wall faces, door swings, window glass, dimension chains on every face, a scale bar, a north arrow and a title block
Drawn from the cloud, not traced: walls, openings, dimension chains, room areas, scale bar, north arrow and title block.

At a glance

Short statements, each one checkable. Where there is a number, it was measured, and the guides say on what.

  • N3D draws dimensioned floor plans with sections and elevations from a point cloud automatically, as DXF, PDF and SVG.
  • It finds rooms as volumes of free space, with floor area, volume, height and the doors between them: 31 rooms and 48 openings in 0.35 s on a 43.5 million point scan.
  • It opens IFC2x3 and IFC4 models with a pure-Rust importer: 100,871 entities in about 1 s.
  • It measures a scan against a model as an exact point-to-triangle distance, with conformance bands at 1, 2 and 4 times the tolerance.
  • An as-built review report divides the site into 8 × 8 zones with stable A1-style labels, each pass, fail or no-data.
  • Wall thickness on the plan is within about ±30 mm at the default voxel on a real survey.
  • N3D does not write IFC or Revit models; the DXF, GeoJSON and the room table are what it hands to the modelling tool.

Where N3D fits in a scan-to-BIM workflow

Measure what was built

A floor plan drawn from the points is a survey, not a design: walls between their measured faces, openings where the scan shows them, dimensions to real surfaces. The same run gives sections and elevations, placed where they show the most, and the building as GIS data.

Know the rooms

The scene graph finds each room as a volume of free space and records its floor area, volume, height, perimeter, what it connects to and what it contains. Two surveys can be compared room by room, even before they are aligned.

Check against the model

Loaded beside the scan, an IFC model is measured to its triangles. Conformance bands give a verdict rather than a gradient, the out-of-tolerance regions come worst first, and the report’s review sheet gives every zone a stable label a site team can refer to.

The Scene Graph window at the Spaces level: every room and open space of the building a node, linked through its doors and openings, with the selected space's floor area, volume, height and door count in the footer
Rooms found by flooding the empty space, with their areas, volumes and doors.
A scan compared with its reference, shown as four conformance bands at one, two and four times the tolerance
Conformance bands: a verdict, not a gradient.

Questions

Does N3D create a BIM model from a point cloud?

No. It creates the measured inputs: dimensioned floor plans, sections and elevations in DXF, the building as GeoJSON and Shapefile, and a room table with areas and volumes. It does not write IFC or Revit families.

Can it compare a scan with an IFC model?

Yes. Add the IFC as a layer and use Compare & detect change: the distance is point to triangle, to the model’s surface, with conformance bands and a worst-first list of out-of-tolerance regions. The report turns it into a per-zone as-built review.

Can it draw a plan from an IFC?

Yes. The scene graph and the floor plan read the model’s own storeys, rooms, walls and openings.

How accurate are the extracted walls?

About plus or minus 30 mm in thickness at the default voxel on a real survey. Gaps wider than 0.9 m at corners and 2.2 m at tees are left as surveyed rather than bridged.

The guides behind it

Each guide gives the menu path, the steps, the command-line equivalent and the limits.

See also

About

Why Neurones 3D exists.

Standing inside a 43.5 million point indoor laser scan rendered by Neurones 3D

I have spent years working with point clouds, and for most of them the software got in the way. I wanted one tool I would actually reach for every day: fast enough to enjoy, honest about what it does, and calm to use.

Neurones 3D started in Python, the way most of my research does. It worked, but it was heavy, and heavy tools quietly discourage you from opening them. So I rebuilt everything in Rust, from the file readers to the renderer, with one rule: stay frugal. A single small file, no cloud, no telemetry, billions of points on a laptop that is ten years old. In an age where every tool reaches for a data center, I wanted the opposite.

The goal is simple to say and hard to earn. One package that carries you from capture to understanding to deliverable, without exporting to five other programs along the way. Load a scan, a splat, a building, a phone video. See it, classify it, measure it, and send out something real. Together, in one place.

This is the tool I always wanted. I hope it becomes yours too.

Dr. Florent Poux 3D Geodata Academy

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