File size: 1,621 Bytes
0e4a763 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 | # Mini-KITTI: a 48x48 dataset for tiny Monocular Depth Estimation
## Dataset
This dataset scales down to 48x48 resolution the [Eigen split](https://proceedings.neurips.cc/paper_files/paper/2014/hash/91c56ce4a249fae5419b90cba831e303-Abstract.html) of the KITTI dataset. It contains:
- A training split of 22600 images in 48x48 resolution
- A validation split of 888 images in 48x48 resolution
- A test split of 697 images in 48x48 resolution
All images are annotated with 48x48 depth and disparity maps obtained with the stereo-matching SGM algorithm. For every image, also a 360x360 centered portion of the original ground truth is provided, matching the same field of view of the respective image. If a 48x48 depth prediction related to a 48x48-sized image is upscaled to 360x360 resolution, these ground truth portions can be used to compute the prediction accuracy.
## Usage
To use the dataset, simply:
~~~
tar -xzvf kitti_48x48.tar.gz -C ./
~~~
Then, you can find the dataset under "kitti_48x48/".
## License
This dataset is derived from [KITTI](https://www.cvlibs.net/datasets/kitti/), and therefore released under [CC-BY-NC-SA-3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) license.
## Citation
If you use this dataset, please cite:
~~~
@article{nadalini2025multi,
title={Multi-modal On-Device Learning for Monocular Depth Estimation on Ultra-low-power MCUs},
author={Nadalini, Davide and Rusci, Manuele and Cereda, Elia and Benini, Luca and Conti, Francesco and Palossi, Daniele},
journal={arXiv preprint arXiv:2512.00086},
year={2025}
}
~~~
|