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.
Reading is free. Exporting computed columns, and the Export with its knowledge… shortcut, are Pro. Converting a cloud as it came in is free.
When to use it
Before deciding what to run (does the file already carry a classification or intensity?), after running something (did it write the column you expected?), and before exporting (which columns will the recipient get?).
Step by step
- Select a layer in the Scene pane. Inspector shows its Elements, Footprint (width × depth in metres), Height, Elevation range, Density in points per cubic metre, and Origin.
- Knowledge lists every column. Each row is tagged file (it came in the source) or N3D (computed here), with the process that made it:
segmentfrom segmentation,classandconfidencefrom the classifier,componentandprimitive_kindfrom decomposition,hagandgroundifyfrom the ground filter,deviationfrom Compare,m3c2_changefrom change detection. - Click a distribution (segment sizes, the Classes strip, the Primitives strip) to open the Distribution window: one sortable row per segment, class or component with points, extent and mean height. Click a row to solo the class or fly to the component.
- Results lists what has been built (scene graph, floor plan, components, terrain products, ground fit, comparison, transferred column, kept measurements), each with an open button to bring its window back.
- To export, use File › Export point cloud… (or Export with its knowledge… under Knowledge, which ticks every field). Choose PLY, LAS, CSV, USD or Web, choose all points or a uniform subsample, and tick the label columns and scalar fields to write.
- Press Export…. The status bar reports how many points were written and where.
What it was measured at
Class ids fill LAS’s 0 to 255 classification byte; segment, instance and component ids are written as 32-bit unsigned extra bytes and scalars as 32-bit floats, and the LAS round trip is checked with laspy.
From the command line
# the command line writes clouds from the process that made them, for example:
n3d-render-cli ground --input tile.laz --out tile_ground.las # ground label + hag
n3d-render-cli transfer --from sub.las --to full.las --field classification --out full_labelled.lasLimits, stated
- A LAS classification is one byte. A class column above 255 is refused with a message suggesting PLY or CSV.
- USD and Web exports have point ceilings and carry colours only, not label columns or scalar fields.
- A cloud that was moved by hand is written without its coordinate system, because the stamp would no longer be true.
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.
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.
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.
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.
Related guides
confidence column, and split a powerline corridor into individual trees, spans and poles.
Understand the scene
Decompose into primitives
Turn a scene of millions of points into a few hundred pieces a person can actually review (planes by size, cylinders, linear runs and clusters), each point carrying its component and its primitive_kind.