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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.

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TUMFTM/PointCloudCrafter

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pointcloudcrafter

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.

Linux Docker ROS2humble PyPI docs Tested on

Install

    pip install pointcloudcrafter

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

Cloning without test data (recommended)

This repository uses Git LFS for large test files (22 MB). Most users do not need them.

Clone without downloading LFS files:

    GIT_LFS_SKIP_SMUDGE=1 git clone https://github.com/TUMFTM/PointCloudCrafter.git

To download them later:

    git lfs pull

Contact

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

About

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.

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