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L-CAS 3D Point Cloud Annotation Tool

Build Status Codacy Badge License: GPL v3

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The tool provides a semi-automatic labeling function, means the 3D point cloud data (loaded from the PCD file) is first clustered to provide candidates for labeling, each candidate being a point cluster. Then, the user annotating the data, can label each object by indicating candidate's ID, category, and visibility. A flowchart of this process is shown below.

flowchart

The quickest way to activate the optional steps is to modify the source code and recompile. 😱

Compiling (tested on Ubuntu 14.04/16.04)

Prerequisites

  • Qt 4.x: sudo apt-get install libqt4-dev qt4-qmake
  • VTK 5.x: sudo apt-get install libvtk5-dev
  • PCL 1.7: sudo apt-get install libpcl-1.7-all-dev

Build and run

  • mkdir build
  • cd build
  • cmake ..
  • make
  • ./cloud_annotation_tool

Test examples

lcas_simple_data.zip contains 172 consecutive frames (in .pcd file) with 2 fully annotated pedestrians.

Citation

If you are considering using this tool and the data provided, please reference the following:

@article{yz19auro,
   author = {Zhi Yan and Tom Duckett and Nicola Bellotto},
   title = {Online learning for 3D LiDAR-based human detection: Experimental analysis of point cloud clustering and classification methods},
   journal = {Autonomous Robots},
   year = {2019}
}

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  • C++ 93.4%
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