L-CAS 3D Point Cloud Annotation Tool
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.
The quickest way to activate the optional steps is to modify the source code and recompile. :scream:
sudo apt-get install libqt4-dev qt4-qmake
sudo apt-get install libvtk5-dev
sudo apt-get install libpcl-1.7-all-dev
mkdir build
cd build
cmake ..
make
./cloud_annotation_tool
lcas_simple_data.zip contains 172 consecutive frames (in .pcd file) with 2 fully annotated pedestrians.
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}
}