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KITTI3D Instance Segmentation DevKit

Welcome to the devkit of the KITTI3D Instance Segmentation annotations.

Example image

The instance segmentation annotations, which are matched to the already annotated 3D bounding boxes of the KITTI3D dataset, are proveded as part of the paper: MonoCInIS: Camera Independent Monocular 3D Object Detection using Instance Segmentation

This repository contains some info on the annotation format and example code for visualising the instances.

Annotation format

For every image of the KITTI3D dataset (7481 training images), we manually annotated all vehicle and pedestrian instances. The annotations are provided as single channel .png files, where the pixels of each instance have a unique id. To link each instance to its corresponding KITTI3D bounding box, we use following convention:

IDCLASS
0Background
1000-1999Vehicle which is linked to a 3D bbox. <br> (The number ID%1000 is the line number of the bbox.txt annotation.)
2000-2999Pedestrian which is linked to a 3D bbox. <br> (The number ID%1000 is the line number of the bbox.txt annotation.)
3000-3999Vehicle or pedestrian which has no 3D bbox annotation.

Download

Download the original dataset from the official KITTI website.

Download our instance segmentation annotations here (15MB).

Usage

To get you started, we provide some example code to load and visualise our instance annotations and the corresponding bbox annotations.

Check out the provided IPython Notebook to visualise our annotations.

Paper

If you find these annotations useful, please cite our paper:

MonoCInIS: Camera Independent Monocular 3D Object Detection using Instance Segmentation

Jonas Heylen, Mark De Wolf, Bruno Dawagne, Marc Proesmans, Luc Van Gool, Wim Abbeloos, Hazem Abdelkawy, Daniel Olmeda Reino

@inproceedings{heylen2021monocinis,
  title={MonoCInIS: Camera Independent Monocular 3D Object Detection using Instance Segmentation},
  author={Heylen, Jonas and De Wolf, Mark and Dawagne, Bruno and Proesmans, Marc and Van Gool, Luc and Abbeloos, Wim and Abdelkawy, Hazem and Reino, Daniel Olmeda},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={923--934},
  year={2021}
}

License

<a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.