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

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+). No GPU? See the Modal question below.
• Python 3.9 or newer
• About 10 GB of disk per model

sem install shows the download size, the license, and a GPU check, then asks once before any bytes move. It is safe to interrupt and resumes where it stopped. After install, inference runs fully offline.

Models

dales-utonia is trained on DALES aerial LiDAR and predicts 8 classes: ground, vegetation, buildings, cars, trucks, poles, powerlines, fences. Its license is CC-BY-NC 4.0 (non-commercial) and is shown before install.

sem models lists what is available and installed. Bare sem opens an interactive picker.

How do I make it more accurate?

Use a preset. Each one turns on more of the same three ideas: predict the scene from several augmented views and vote (test-time augmentation), tile the scene a second time at a half offset so no point sits on a tile edge (overlapped voting), and clean up the labels afterwards (smoothing, island removal, geometry rules).

• default: one pass with light smoothing
• --med: 4 voting views, overlapped tiling, island removal. Roughly 4x the time.
• --high: 6 views including flips and rotations, stronger smoothing, geometry rules for ground/vegetation/building confusions, per-class probability fields. Roughly 6x.
• --ultra: 9 views and the strongest smoothing. Roughly 9x.

An explicit flag always wins over a preset, so --ultra --no-sieve means ultra without island removal.

How do I make it faster?

--low is a single pass with no cleanup, the fastest option. If a preset is mostly what you want, --no-overlap drops its second tiling pass, which is about half its extra cost.

Big buildings come out patchy or cut through. Why?

The scene is processed in square tiles, typically 50 m on a side. An object bigger than one tile is predicted in pieces, and the pieces can disagree. Two fixes that combine well:

• --chunk-xy 100 makes the tiles bigger, so a large building fits in one. Costs GPU memory.
• --overlap (on automatically with --med and up) predicts a second pass at a half offset and votes, which removes most seam artifacts.

It ran out of GPU memory

Lower --chunk-xy, try 35 and then 25. Smaller tiles need less VRAM, and the extra seams they create are what --overlap is for.

Poles or powerlines are disappearing

Presets from --med up turn on island removal, which absorbs clusters smaller than 10 points into their surroundings. Thin objects are exactly small clusters. Keep the filter but make it gentler with --sieve-min-pts 5, or turn it off with --no-sieve.

Can I hide the model's low-confidence guesses?

--unclass 0.6 exports every point below 60% confidence as unclassified instead of its best guess. To see where the model is unsure, --entropy adds a 0..1 uncertainty field, --margin adds the gap between the top two classes, and --prob-dims adds one probability field per class (grows the file).

I want individual objects, not just classes

--panoptic vehicle:5x2.5 splits a class into instances using a typical footprint in meters (length x width) and writes an instance_id per point. Several classes at once: --panoptic vehicle:5x2.5,pole:1x1. Based on ALPINE, no extra training involved.

Can I combine models?

sem infer dales-utonia+dales-hag-utonia tile.las runs every model in the chain and merges their predictions with a vote: each model's per-class probabilities are averaged, and the strongest combined evidence wins. Where the models agree, the label sticks; exact ties go to the model you listed first, so lead with your strongest. The result carries an agreement field (what fraction of models agreed on each point) and an ens_member field (which model drove each label). The models must share the same class list. Each member also keeps its own tile_<model>_predictions folder, so you can compare them individually.

My file has no coordinate system

--epsg 32610 declares it (use your zone's code). Only needed when the file itself does not say.

I don't have a GPU

sem infer dales-utonia tile.las --modal runs the GPU work in your own Modal account. One-time setup: pip install modal, then modal setup. Conversion and export still happen on your machine; only the prepared tiles travel.

Can I rerun with different settings without reconverting?

Yes. The output folder is a self-contained job, named after your file and the model (tile_dales-utonia_predictions/). Quality options (presets, TTA, cleanup, export) never reconvert. Conversion options (--epsg, --ground-method, --hag, ...) and changes to the input file itself reconvert automatically; identical settings reuse the staged files. Every model keeps its own job folder, so switching models never mixes results.

A whole folder of tiles?

Pass the folder. Every .las/.laz/.ply/.pcd inside becomes one job, and results land in <model>_predictions/ inside it.

Where does everything live?

Downloads go to ~/.trainer (set TRAINER_HOME to move them). Your data and results never go there: each job is a folder next to your input, or under a default you set with sem output <dir>. sem clean dales-utonia removes one model; sem clean --all removes everything sem ever downloaded.

Every option

sem infer --help documents all of it. Add --pick to browse and edit every option with arrow keys before running.

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.