3dsem

Classify point clouds with pretrained 3D semantic segmentation models, on your own machine, from one command.

pip install 3dsem

sem install dales-utonia
sem infer dales-utonia tile.las

That's the whole workflow. The classified .laz appears next to your input file, with per-point classification, confidence, and every original dimension carried over.

What you need

• An NVIDIA GPU with a current driver (Windows 527.41+, Linux 525.60.13+), or a Modal account for cloud runs with --modal
• Python 3.9 or newer
• About 10 GB of disk per model

sem install downloads a model together with its exact, tested runtime in one step. After install, inference runs fully offline.

Models

Commands

sem                  interactive picker (choose a model, run it)
sem models           list available and installed models
sem install <model>  one-time model download
sem infer <model> <input> [output]
sem output [<dir> | off]   default output directory (unset: results land
                           next to the input)
sem clean [<model> | --all]

infer accepts a .las/.laz/.ply/.pcd file or a whole folder, and converts it automatically using the model's own training recipe. By default it does a fast single pass with light smoothing; the effort presets trade time for accuracy:

sem infer dales-utonia tile.las --ultra            max accuracy: 9-view TTA,
                                                   full cleanup (~9x time;
                                                   also --med, --high, --low)
sem infer dales-utonia tile.las --unclass 0.6      low-confidence points
                                                   become unclassified
sem infer dales-utonia tile.las --panoptic vehicle:5x2.5
                                                   split a class into
                                                   individual objects
sem infer dales-utonia tile.las --modal            run on Modal instead of
                                                   a local GPU
sem infer dales-utonia tile.las --pick             browse every option with
                                                   arrow keys, then run

Every option is documented in sem infer --help.

What the extras are

• TTA & overlapped voting — the model predicts the scene from several augmented views and overlapping tiles, then votes; slower, more robust.
• Cleanup passes — KNN probability smoothing (in the spirit of RangeNet++), small-island removal, and height/planarity rules for ground/vegetation/building confusions.
• Height Above Ground — when a model uses it, ground is detected with CSF or SMRF and the channel is computed from your cloud automatically.
• --panoptic — training-free instance clustering with ALPINE (paper): give a typical object footprint per class and get an instance_id per point.

Where things live

All downloads go to ~/.trainer (set TRAINER_HOME to move them); sem clean --all removes everything. Your data and results never go there — each job is one folder next to your input (or under your sem output dir): the staged+classified .npz, a job.json record, and the exported .laz.

Licensing

The sem tool is MIT licensed. Each model ships a NOTICE.md stating its architecture credits and license terms; some models carry a non-commercial restriction inherited from their pretrained components, shown before you install.