Metadata-Version: 2.4
Name: truckscenes-devkit
Version: 1.2.0
Summary: Official development kit of the MAN TruckScenes dataset (www.man.eu/truckscenes).
Home-page: https://github.com/TUMFTM/truckscenes-devkit
Author: Felix Fent, Fabian Kuttenreich, Florian Ruch, Farija Rizwin
Author-email: truckscenes@man.eu
License: Apache-2.0
Keywords: MAN,TruckScenes,dataset,devkit,perception
Platform: linux
Platform: windows
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: <3.12,>=3.8
Description-Content-Type: text/markdown
Requires-Dist: numpy
Requires-Dist: pyquaternion>=0.9.5
Requires-Dist: tqdm
Requires-Dist: pypcd4
Provides-Extra: all
Requires-Dist: matplotlib; extra == "all"
Requires-Dist: jupyter; extra == "all"
Requires-Dist: open3d; extra == "all"
Requires-Dist: opencv-python; extra == "all"
Requires-Dist: Pillow>6.2.1; extra == "all"
Provides-Extra: visu
Requires-Dist: matplotlib; extra == "visu"
Requires-Dist: open3d; extra == "visu"
Requires-Dist: opencv-python; extra == "visu"
Requires-Dist: Pillow>6.2.1; extra == "visu"

<div align="center">

<h1>MAN TruckScenes devkit</h1>

World's First Public Dataset For Autonomous Trucking

[![Python](https://img.shields.io/badge/python-3-blue.svg)](https://www.python.org/downloads/)
[![Linux](https://img.shields.io/badge/os-linux-blue.svg)](https://www.linux.org/)
[![Windows](https://img.shields.io/badge/os-windows-blue.svg)](https://www.microsoft.com/windows/)
[![arXiv](https://img.shields.io/badge/arXiv-Paper-blue.svg)](https://arxiv.org/abs/2407.07462)

[![Watch the video](https://raw.githubusercontent.com/ffent/truckscenes-media/main/thumbnail.jpg)](https://cdn-assets-eu.frontify.com/s3/frontify-enterprise-files-eu/eyJwYXRoIjoibWFuXC9maWxlXC9lb2s3TGF5V1RXMXYxZU1TUk02US5tcDQifQ:man:MuLfMZFfol1xfBIL7rNw0W4SqczZqwTuzhvI-yxJmdY?width={width}&format=mp4)

</div>

## Overview
- [Website](#website)
- [Installation](#installation)
- [Setup](#setup)
- [Usage](#usage)
- [Citation](#citation)

<div id="website"></div>  

## 🌐 Website
To read more about the dataset or download it, please visit [https://www.man.eu/truckscenes](https://www.man.eu/truckscenes)

<div id="installation"></div>  

## 💾 Installation
Our devkit is available and can be installed via pip:
```
pip install truckscenes-devkit
```

If you also want to install all the (optional) dependencies for running the visualizations:
```
pip install "truckscenes-devkit[all]"
```

For more details on the installation see [installation](./docs/installation.md)

<div id="setup"></div> 

## 🔨 Setup
Download **all** archives from our [download page](https://www.man.eu/truckscenes/) or the [AWS Open Data Registry](https://registry.opendata.aws/).  

Unpack the archives to the `/data/man-truckscenes` folder **without** overwriting folders that occur in multiple archives.  
Eventually you should have the following folder structure:
```
/data/man-truckscenes
    samples	-	Sensor data for keyframes.
    sweeps	-	Sensor data for intermediate frames.
    v1.0-*	-	JSON tables that include all the meta data and annotations. Each split (trainval, test, mini) is provided in a separate folder.
```

<div id="usage"></div> 

## 🚀 Usage
Please follow these steps to make yourself familiar with the MAN TruckScenes dataset:
- Read the [dataset description](https://www.man.eu/truckscenes/).
- Explore the dataset [videos](https://cdn-assets-eu.frontify.com/s3/frontify-enterprise-files-eu/eyJwYXRoIjoibWFuXC9maWxlXC9lb2s3TGF5V1RXMXYxZU1TUk02US5tcDQifQ:man:MuLfMZFfol1xfBIL7rNw0W4SqczZqwTuzhvI-yxJmdY?width={width}&format=mp4).
- [Download](https://www.man.eu/truckscenes/) the dataset from our website.
- Make yourself familiar with the [dataset schema](./docs/schema_truckscenes.md)
- Run the [tutorial](./tutorials/truckscenes_tutorial.ipynb) to get started:
- Read the [MAN TruckScenes paper](https://arxiv.org/abs/2407.07462) for a detailed analysis of the dataset.

<div id="citation"></div> 

## 📄 Citation
```
@inproceedings{truckscenes2024,
 title = {MAN TruckScenes: A multimodal dataset for autonomous trucking in diverse conditions},
 author = {Fent, Felix and Kuttenreich, Fabian and Ruch, Florian and Rizwin, Farija and Juergens, Stefan and Lechermann, Lorenz and Nissler, Christian and Perl, Andrea and Voll, Ulrich and Yan, Min and Lienkamp, Markus},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {A. Globerson and L. Mackey and D. Belgrave and A. Fan and U. Paquet and J. Tomczak and C. Zhang},
 pages = {62062--62082},
 publisher = {Curran Associates, Inc.},
 url = {https://proceedings.neurips.cc/paper_files/paper/2024/file/71ac06f0f8450e7d49063c7bfb3257c2-Paper-Datasets_and_Benchmarks_Track.pdf},
 volume = {37},
 year = {2024}
}
```

_Copied and adapted from [nuscenes-devkit](https://github.com/nutonomy/nuscenes-devkit)_
