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 (tile_predictions/tile_pred.laz), with per-point classification, confidence, and the original dimensions 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 for a model

Everything else is handled for you: sem install downloads a model together with its exact, tested runtime in one step. After install, inference runs fully offline; nothing else ever downloads.

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 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. Common extras:

sem infer dales-utonia tile.las --ultra            max accuracy: 9-view TTA,
                                                   APCoTTA, 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

Every option is documented in sem infer --help.

Where things live

All downloads go to ~/.trainer. Set the TRAINER_HOME environment variable to put them somewhere else. sem clean --all removes everything.

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.