A toolkit for extracting, manipulating, and evaluating point clouds and 3D spatial maps. Includes functions for processing, analyzing, and visualizing point clouds, designed to streamline workflows in 3D mapping and general point cloud handling. Ideal for researchers and developers working with LiDAR, SLAM, and 3D spatial data.

Install

pip install pointcloudcrafter

‍Supported platform: Ubuntu 24.04 on x86_64 with CPython 3.12. The published PyPI wheel is built for manylinux_2_39_x86_64 / cp312 only. On other platforms, use the Docker image or build from source.

Usage

We provide a standalone Pip package, which is self-contained, so you do not have to worry about any dependencies and possible conflicts. We also provide the tool as ROS2 package. Both feature the full functionality, so you can decide what suits your needs best.

For rosbag-processing:

pointcloudcrafter-rosbag -h
 
ros2 run pointcloudcrafter rosbag -h

For file-processing:

pointcloudcrafter-file -h
 
ros2 run pointcloudcrafter file -h

Documentation

For more details on the features and how to use them, take a look at the documentation hosted on GitHub Pages:
https://TUMFTM.github.io/PointCloudCrafter

Test Data

A small ROS 2 bag and example transforms are published as a pinned release asset on this repository. To download the current test data:

curl -L https://github.com/TUMFTM/PointCloudCrafter/releases/download/testdata-v1/test-data.tar.gz \

| tar xz -C /path/to/pointcloudcrafter-root/

The same asset is used by the CI test job, so you get exactly the data the project is validated against. When the test data is updated, the tag is bumped (e.g. testdata-v2).

Contact

Dominik Kulmer
Maximilian Leitenstern
Institute of Automotive Technology, School of Engineering and Design, Technical University of Munich, 85748 Garching, Germany