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Merge branch 'main' of https://github.com/vita-epfl/monoloco into main

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# Monoloco library    [![Downloads](https://pepy.tech/badge/monoloco)](https://pepy.tech/project/monoloco)
# Monoloco library      [![Downloads](https://pepy.tech/badge/monoloco)](https://pepy.tech/project/monoloco)
<img src="docs/monoloco.gif" alt="gif" />
This library is based on three research projects for monocular/stereo 3D human localization (detection), body orientation, and social distancing.
This library is based on three research projects for monocular/stereo 3D human localization (detection), body orientation, and social distancing. Check the [demo video](https://www.youtube.com/watch?v=O5zhzi8mwJ4)!
> __MonStereo: When Monocular and Stereo Meet at the Tail of 3D Human Localization__<br />
> _[L. Bertoni](https://scholar.google.com/citations?user=f-4YHeMAAAAJ&hl=en), [S. Kreiss](https://www.svenkreiss.com),
[T. Mordan](https://people.epfl.ch/taylor.mordan/?lang=en), [A. Alahi](https://scholar.google.com/citations?user=UIhXQ64AAAAJ&hl=en)_, ICRA 2021 <br />
__[Article](https://arxiv.org/abs/2008.10913)__ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; __[Citation](#Citation)__ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; __[Video](#Todo)__
__[Article](https://arxiv.org/abs/2008.10913)__ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; __[Citation](#Citation)__ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; __[Video](https://www.youtube.com/watch?v=pGssROjckHU)__
<img src="docs/out_000840_multi.jpg" width="700"/>
@ -188,7 +188,7 @@ The network estimates orientation and box dimensions as well. Results are saved
<br />
## Training
We train on the KITTI dataset (MonoLoco/Monoloco++/MonStereo) or the nuScenes dataset (MonoLoco) specifying the path of the json file containing the input joints. Please download them [heere](https://drive.google.com/file/d/1e-wXTO460ip_Je2NdXojxrOrJ-Oirlgh/view?usp=sharing) or follow [preprocessing instructions](#Preprocessing).
We train on the KITTI dataset (MonoLoco/Monoloco++/MonStereo) or the nuScenes dataset (MonoLoco) specifying the path of the json file containing the input joints. Please download them [here](https://drive.google.com/file/d/1bJPyA1HuX9uyJYf1IhiDqzhkvSokd4l0/view?usp=sharing) or follow [preprocessing instructions](#Preprocessing).
Results for MonoLoco++ are obtained with:
@ -321,8 +321,6 @@ python -m monoloco.run eval \
--save \
````
<img src="docs/results_monoloco_pp." width="550"/>
By changing the net and the model, the same command evaluates MonStereo model.
<img src="docs/results_stereo.jpg" width="550"/>
@ -355,7 +353,7 @@ When using this library in your research, we will be happy if you cite us!
@InProceedings{bertoni_2021_icra,
author = {Bertoni, Lorenzo and Kreiss, Sven and Mordan, Taylor and Alahi, Alexandre},
title = {MonStereo: When Monocular and Stereo Meet at the Tail of 3D Human Localization},
booktitle = {International Conference on Robotics and Automation (ICRA)},
booktitle = {the International Conference on Robotics and Automation (ICRA)},
year = {2021}
}
```
@ -372,7 +370,7 @@ When using this library in your research, we will be happy if you cite us!
@InProceedings{bertoni_2019_iccv,
author = {Bertoni, Lorenzo and Kreiss, Sven and Alahi, Alexandre},
title = {MonoLoco: Monocular 3D Pedestrian Localization and Uncertainty Estimation},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
booktitle = {the IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}