travel-seg
Python bindings for TRAVEL — traversable ground and above-ground object segmentation for 3D LiDAR scans.
Install
From PyPI (recommended)
pip install travel-seg
Prebuilt wheels are published for Linux x86_64 (manylinux_2_17), macOS arm64 + x86_64, and Windows x86_64 for CPython 3.8–3.13. As of v1.1, the only system dependency is Eigen — no PCL / Boost install required.
From source (development)
# System deps (Ubuntu)
sudo apt install build-essential cmake libeigen3-dev
# System deps (macOS)
brew install cmake eigen
# System deps (Windows)
vcpkg install eigen3:x64-windows
# then: -DCMAKE_PREFIX_PATH="C:/vcpkg/installed/x64-windows" forwarded by
# the release workflow / pip env on Windows.
git clone https://github.com/url-kaist/TRAVEL.git
cd TRAVEL
pip install -e python/
Usage
import numpy as np
import travel_seg as ts
# KITTI .bin → (N, 4) float32 XYZI
points = np.fromfile("0000.bin", dtype=np.float32).reshape(-1, 4)
# One-shot
result = ts.segment(points)
ground_pts = points[result.ground_mask]
for cid in np.unique(result.instance_labels):
if cid == 0:
continue
cluster_pts = points[result.instance_labels == cid]
print(f"cluster {cid}: {len(cluster_pts)} points")
# Per-frame loops: build the segmenters once, reuse
tgs = ts.TravelGroundSeg(ts.GroundSegConfig(max_range=80.0))
aos = ts.ObjectCluster(ts.ObjectClusterConfig(vert_scan=64, horz_scan=4500))
for frame in frames:
ground_mask, elapsed = tgs.estimate_ground(frame[:, :3])
labels = aos.segment_objects(frame[~ground_mask, :3])
...
API
| Symbol | Purpose |
|---|---|
GroundSegConfig |
dataclass — TGS parameters; KITTI-tuned defaults |
ObjectClusterConfig |
dataclass — AOS parameters; KITTI-tuned defaults |
TravelGroundSeg(cfg) |
reusable ground segmenter; .estimate_ground(points) -> (mask, elapsed) |
ObjectCluster(cfg) |
reusable object clusterer; .segment_objects(points) -> labels |
segment(points, ground_config=None, object_config=None) |
one-shot helper returning a SegmentResult |
SegmentResult |
dataclass: .ground_mask, .instance_labels, .elapsed_seconds |
Inputs are float32 numpy arrays of shape (N, 3) (XYZ) or (N, 4) (XYZI). The intensity column is currently ignored by both TGS and AOS but accepted for convenience when reading raw KITTI bins.
Outputs are length-N numpy arrays aligned with the original input order:
ground_mask:bool,Truewhere the input point was classified as ground.instance_labels:int32.0= ground / unclassified / dropped.1..K= per-cluster ids within this single frame (not consistent across frames).
Tests / examples
pip install -e python/[test]
pytest python/tests/
# Real data demo
python python/examples/run_kitti.py /path/to/kitti/sequences/00 0 /tmp/out
Citation
See the top-level README.
Release process
Publishing uses PyPI Trusted Publishing (OIDC), so there are no API tokens or GitHub secrets to manage. PyPI authenticates the GitHub Actions workflow directly based on a one-time pending-publisher registration.
One-time PyPI setup
Visit https://pypi.org/manage/account/publishing/ and add a pending publisher with these values (only required before the very first release; it auto-graduates to a regular trusted publisher after the first upload):
| Field | Value |
|---|---|
| PyPI Project Name | travel-seg |
| Owner | url-kaist |
| Repository name | TRAVEL |
| Workflow name | release.yml |
| Environment name | pypi |
GitHub side: create an environment named pypi at Settings -> Environments
(no secrets/protection rules needed unless you want manual approval).
Per-release flow
- Bump version in three places to keep them in sync:
python/pyproject.toml->[project] versionpython/travel_seg/__init__.py->__version__python/travel_seg/pybind/travel_pybind.cpp->m.attr("__version__")
- Commit, tag, push:
git commit -am "release: travel-seg vX.Y.Z" git tag vX.Y.Z git push origin main vX.Y.Z
- Trigger the Release (PyPI) workflow from the GitHub Actions UI.
- Verify the install in a fresh venv:
python -m venv /tmp/check && source /tmp/check/bin/activate pip install travel-seg==X.Y.Z python -c "import travel_seg as ts; import numpy as np; \ ts.segment(np.random.uniform(-10,10,(2000,3)).astype(np.float32))"
1 maintainer
Metadata
1 maintainer
Release files for travel-seg 1.1.0
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Built distributions (wheels)
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