Semantic KITTI Dataset Statistics

This repository holds a script that allows an analysis of the Semantic KITTI Dataset [1,2]. The main focus is on distance and label analysis. For all statistics a csv file and a plot are generated.

Some Examples:

see which label has how many points over the distance

see how many points belong to a specific label in each sequence

... and many more, such as the analysis per sequence or labels over azimuth and elevation angle

Getting Started
Running the code
License
References

Getting Started

These instructions will get you a copy of the project up and running on your local machine.

Installing

$ git clone https://github.com/ltriess/semantic_kitti_stats.git
$ cd semantic_kitti_stats
$ pip install requirements.txt

Get the Data

Download the data and unzip it in the same folder.

for the labels: Semantic KITTI
for the point clouds: KITTI Odometry

Running the code

The main script is analyse.py which can be called according to

Usage: analyse_sequence.py [OPTIONS] PATH

Options:
  --mode [compute|from_data]  If compute is selected, PATH must be the path to the dataset.
                              All statistics will be calculated from the data. If from_data
                              is selected, PATH must be a a folder in which csv files with
                              the computed statistics are located.
  --save_dir PATH             Path where to save the generated graphs. If not provided, show on display.
  --help                      Show this message and exit.

The script first iterates over all trainval sequences and generates separate statistics for each sequence. Finally, all the sequence statistics are combined and a total analysis as well as a sequence overview is generated. There are two modes in which the script dan be called:

compute: PATH must point to the root directory of the dataset which contains the folders dataset/sequences/{00..10}/{velodyne/labels} according to how the dataset is extracted after the download. All statistics will be computed from the dataset and then plots will be generated. If save_dir is set to a valid path, all the statistics will be saved to csv files for later usage.
from_data: PATH must point to the folder in which all the generated csv files are located. This is useful when the statistics are available, but a redo of the plots is needed.

In both modes, if save_dir is set, the plots are saved as png files to the specified location. If it is not set, the plots will be displayed on the screen.

License

This project is licensed under the MIT License - see the LICENSE file for details

References

[1] J. Behley and M. Garbade and A. Milioto and J. Quenzel and S. Behnke and C. Stachniss and J. Gall, "SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences", ICCV 2019

[2] A. Geiger and P. Lenz and C. Stiller and R. Urtasun, Vision meets Robotics: The KITTI Dataset, IJRR 2013

Youngsplace0913 / semantic_kitti_stats