Notices

L-CAS 3D Point Cloud Annotation Tool

Build Status Codacy Badge License: GPL v3

screenshot

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. :scream:

Compiling (tested on Ubuntu 14.04/16.04)

Prerequisites

Build and run

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}
}