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Export a point cloud (LAS, PLY, CSV, USD, web)

One dialog for every point cloud export - format, subsample, colours, label columns and scalar fields - with a LAS writer that puts the class in the classification byte and every id in an exact 32-bit extra byte.

Where File › Export point cloud… Tier Pro

Converting a cloud as it came in is free; writing what N3D computed (the label columns and computed scalars) is Pro.

Part of Point cloud classification, Point cloud segmentation, Gaussian splat to floor plan and CAD.

The Export Point Cloud window with LAS chosen: points, colours, label columns for segment, class, instance and primitive ids, scalar fields, and the note on how LAS stores them
One dialog for every export. With LAS, the class fills the classification byte and ids ride as 32-bit extra bytes.

When to use it

When the result leaves N3D: a classified cloud for a GIS, a cloud with height above ground for a forestry model, component ids for a digital twin, a quick web viewer for a client.

Step by step

  1. Open File › Export point cloud… (or Export with its knowledge… under Knowledge, which ticks every field).
  2. Choose the Format: PLY (normals, labels and scalar fields), LAS (the survey standard), CSV (one row per point), USD (a text .usda stage) or Web (one self-contained .html viewer, 9 bytes a point, no network access).
  3. Under Points, keep All points or take a uniform subsample of 10 or 1 million.
  4. Under Label columns, tick what to write: Segment ids, Class ids, Deviation, Instance ids, Primitives (component and primitive_kind) and Baseline classes.
  5. Under Scalar fields, tick the columns the file carried and the ones N3D computed, such as hag or confidence.
  6. Press Export…. The status reads Exported n points » path.

What it was measured at

Measured

The LAS round trip is checked with laspy: the class fills the 0 to 255 classification byte, ids ride as u32 extra bytes (data type 5) and scalars as f32 (data type 9). LAS 1.2 is written when every class fits in 5 bits, LAS 1.4 when a class exceeds 31 or a coordinate system is present.

Limits, stated

  • A class column above 255 is refused for LAS, with the suggestion to use PLY or CSV.
  • USD and Web exports have point ceilings and carry colours only; the dialog warns before the write would be refused.
  • The coordinate system is written only while the geometry is still the source’s; a layer moved by hand is written without it.
  • OBJ is not written; meshes leave as glTF / GLB from their own export.

Related questions

What goes into a LAS export?

The class column fills LAS’s classification byte (0 to 255). Segment, instance and component ids are written as exact 32-bit unsigned extra bytes, and computed scalars such as height above ground or deviation as 32-bit floats, so PDAL, laspy and CloudCompare read them back. Intensity, returns, point source and scan angle keep their standard slots. The file is LAS 1.2 unless a class exceeds 31 or a coordinate system is present, in which case it is LAS 1.4.

Which formats can it write?

Point clouds as PLY, LAS and CSV, a USD stage (.usda) or a self-contained web viewer. Meshes and voxels as glTF / GLB. Drawings as SVG, DXF (R2000) and PDF 1.4. Vectors as GeoJSON and Shapefile. Rasters as GeoTIFF and ESRI ASCII grid. Reports as HTML. Images as PNG or JPEG and flythroughs as MP4. Writing results out is a Pro feature.

Does it keep the coordinate system?

Yes. It is read from LAS GeoTIFF keys, the OGC WKT record and LAS 1.4 extended records, carried into reports, and stamped on a LAS export as long as the geometry is still the source’s. A cloud that was moved by hand is written without it, because the stamp would no longer be true. Vector layers are checked against it and a clash is reported.

What does a paid licence unlock?

Writing out what the program worked out: computed columns (class, segment, confidence, height above ground, deviation) on an export, drawings (DXF, PDF, SVG), terrain rasters, GIS vectors, meshes as glTF, reports, region and zone CSVs, saved images and flythrough videos. Desktop, Engine & SDK and the academic licence unlock the same features in the program; Engine & SDK adds the Python package and the command line. The boundary is enforced in code and tested in both directions.

More in the FAQ.

Load and view Inspector, Knowledge and export columns The left pane says what a dataset is (its footprint, height and density), what it knows (every column, from the file or computed by N3D) and what has been built from it, and the export dialog writes exactly that. Understand the scene Classify a point cloud (four models) Label every point with one of four trained models (Indoor, Outdoor aerial LiDAR, Powerlines, Streetscape), read how sure the model was from a per-point confidence column, and split a powerline corridor into individual trees, spans and poles. Deliver Reports One self-contained HTML document from what the session measured - the as-built review with its per-zone punch list, the volume and how much of it rests on the classification, the measurements you kept - and a provenance block that lets someone else re-derive every number.

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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