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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<meta name="description"
content="A Dataset for Infrastructure-Supported Perception Research with Focus on Public Transportation.">
<meta name="keywords" content="CoopScenes, AEIF, OPNV, dataset">
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<title>CoopScenes Collection</title>
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More Research
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BlurScene
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<section class="hero">
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<h1 class="title is-1 publication-title">CoopScenes: Multi-Scene Infrastructure and Vehicle Data for Advancing Collective Perception in Autonomous Driving</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://github.com/MarcelVSHNS">Marcel Vosshans</a><sup>1,2</sup>,</span>
<span class="author-block">
<a href="https://github.com/AlexanderBMN">Alexander Baumann</a><sup>1</sup>,</span>
<span class="author-block">
<a href="https://scholar.google.de/citations?user=XRtrEuwAAAAJ&hl=de">Matthias Drueppel</a><sup>3</sup>,
</span>
<span class="author-block">
<a href="https://scholar.google.fr/citations?hl=en&user=NIdLQnUAAAAJ"> Omar Ait-Aider</a><sup>2</sup>,
</span>
<span class="author-block">
<a href="https://scholar.google.fr/citations?hl=en&user=jODWpzQAAAAJ">Youcef Mezouar</a><sup>2</sup>,
</span>
<span class="author-block">
<a href="https://scholar.google.fr/citations?user=MzVfUCQAAAAJ&hl=en">Thao Dang</a><sup>1</sup>
</span>
<span class="author-block">
<a href="https://markus-enzweiler.de/">Markus Enzweiler</a><sup>1</sup>
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><sup>1</sup>University of Esslingen</span>
<span class="author-block"><sup>2</sup>Clermont Auvergne INP / CNRS</span>
<span class="author-block"><sup>3</sup>Cooperative State University Stuttgart</span>
</div>
<div class="column has-text-centered">
<div class="publication-links">
<!-- PDF Link. -->
<span class="link-block">
<a href="https://ieeexplore.ieee.org/document/11097591" target="_blank" rel="noopener noreferrer"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<img src="static/images/IEEE_logo.svg">
</span>
<span>Paper</span>
</a>
</span>
<span class="link-block">
<a href="https://arxiv.org/abs/2407.08261" target="_blank" rel="noopener noreferrer"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<img src="static/images/ArXiv_logo.svg">
</span>
<span>arXiv</span>
</a>
</span>
<!-- Code Link. -->
<span class="link-block">
<a href="https://github.com/MarcelVSHNS/CoopScenes" target="_blank" rel="noopener noreferrer"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fab fa-github"></i>
</span>
<span>Code</span>
</a>
</span>
<!-- Colab Link. -->
<span class="link-block">
<a href="https://colab.research.google.com/drive/1p2cw3bSZ6B798qQ2jVnpvKQI5pv_-y_D?usp=sharing" target="_blank" rel="noopener noreferrer"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<img src="static/images/colab.svg">
</span>
<span> Google Colab</span>
</a>
</span>
<span class="link-block">
<a href="https://huggingface.co/datasets/iis-esslingen/CoopScenes" target="_blank" rel="noopener noreferrer"
class="external-link button is-normal is-rounded is-dark">
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<img src="static/images/huggingface.svg">
</span>
<span>Data</span>
</a>
</span>
</div>
</div>
</div>
</div>
</div>
</div>
</section>
<section class="hero is-light is-small">
<div class="hero-body">
<div class="container">
<div id="results-carousel" class="carousel results-carousel">
<div class="item">
<img src="./imgs/FRONT_LEFT.png" alt="Front left view">
</div>
<div class="item">
<img src="./imgs/STEREO_LEFT.png" alt="Front view">
</div>
<div class="item">
<img src="./imgs/FRONT_RIGHT.png" alt="Front right view">
</div>
<div class="item">
<img src="./imgs/BACK_RIGHT.png" alt="Back right view">
</div>
<div class="item">
<img src="./imgs/BACK_LEFT.png" alt="Back left view">
</div>
<div class="item">
<img src="./imgs/VIEW_1.png" alt="Back left view">
</div>
<div class="item">
<img src="./imgs/VIEW_2.png" alt="Back left view">
</div>
</div>
</div>
</div>
</section>
<section class="section">
<div class="container is-max-desktop">
<!-- Abstract. -->
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
The increasing complexity of urban environments has underscored the potential of effective collective
perception systems. To address these challenges, we present the CoopScenes dataset, a large-scale, multi-scene dataset that provides synchronized sensor data from both the ego-vehicle and the supporting infrastructure.
The dataset provides 104 minutes of spatially and temporally synchronized data at 10 Hz, resulting in 62,000 frames.
It achieves competitive synchronization with a mean deviation of only 2.3 ms.
It includes a novel procedure for precise registration of point cloud data from the ego-vehicle and infrastructure sensors, automated annotation pipelines, and an open-source anonymization pipeline for faces and license plates.
Covering 9 diverse scenes with 100 maneuvers, the dataset features scenarios such as public transport hubs, city construction sites, and high-speed rural roads across three cities in the Stuttgart region, Germany.
The full dataset amounts to 527 GB of data and is provided in the .4mse format and is easily accessible through our comprehensive development kit.
By providing precise, large-scale data, CoopScenes facilitates research in collective perception, real-time sensor registration, and cooperative intelligent systems for urban mobility, including machine learning-based approaches.
</p>
</div>
<div class="content has-text-centered">
<img src="./imgs/Dataset_comparison.png" alt="Datasets">
</div>
</div>
</div>
<!--/ Abstract. -->
<!-- Setup Image -->
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<div class="content">
<h2 class="title is-3">Setup</h2>
<img src="./imgs/setup.png" alt="Dataset setup overview">
</div>
</div>
</div>
<!--/ Setup Image -->
<!-- Setup Description -->
<div class="columns is-centered has-text-centered">
<div class="columns is-centered">
<div class="column is-two-fifths">
<div class="content has-text-justified">
<h4 class="title is-4">Ego-vehicle:</h4>
<p>
Our vehicle agent is a modified Mercedes Sprinter, designed with spacious, bus-like seating and an elevated
roof for easy entry and comfortable standing room inside the cabin. Equipped with a complete AD perception
sensor suite, it features six cameras arranged for 360° coverage, including a secondary front-facing camera
optimized for stereo applications (note: there is no dedicated rear camera). The vehicle is also outfitted
with three LiDAR sensors to capture mid- and near-range perspectives and a high-precision INS system
(integrating GNSS and IMU with correction data) to ensure accurate localization and navigation data.
</p>
</div>
</div>
<div class="column is-two-fifths">
<div class="content has-text-justified">
<h4 class="title is-4">Sensor Tower:</h4>
<p>
Our sensor tower shares the same advanced specification as the vehicle agent, serving as a highly adaptable
observation unit. It features two movable arms, each mounted with a camera and a solid-state LiDAR unit,
alongside a 360° Ouster OS LiDAR (128 rays) positioned at the top for comprehensive coverage. The tower is
equipped with a GNSS system and achieves nanosecond-level synchronization with the vehicle’s data stream
via PTP, using GNSS-triggered timing. This setup ensures precise alignment of all data within a few
milliseconds. Operating independently, the tower relies on a 5G mobile connection, dual 100 Ah (24V)
batteries, and a solar panel, making it fully self-sufficient in the field.
</p>
</div>
</div>
</div>
</div>
<!--/ Setup Description -->
<!-- Locations. -->
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-3">Locations & Recordings</h2>
<div class="content has-text-justified">
<p>
For our initial publication we've gathered data from 9 different locations across Stuttgart, Esslingen,
and Waiblingen in Baden-Württemberg, Germany. Each location is precisely mapped in a
<a href="https://earth.google.com/earth/d/1Pk4AGiighU9QbSpYQ6eyEZuCGaJJz9oq?usp=sharing" target="_blank">Google Earth project</a>,
making it easy to revisit and verify positioning.
</p>
</div>
<div class="content has-text-centered">
<img src="./imgs/scene_book.png" alt="Scene Book">
</div>
<div class="content has-text-justified">
<p>
Given the varying camera placements and angles on our
infrastructure towers, each site requires tailored calibration. At each location, a series of defined
maneuvers was driven multiple times to ensure consistency. In total, we offer around 104 minutes of fully
synchronized and anonymized data, with our anonymization process and a comprehensive development report
to be made publicly available.
</p>
</div>
</div>
</div>
<!--/ Abstract. -->
</div>
</section>
<section class="hero is-light is-small">
<div class="hero-body">
<div class="container">
<div id="recordings-carousel" class="carousel recordings-carousel" data-slides-to-show="4">
<div class="item">
<img src="./imgs/positions/bus.jpg" alt="Bus Agent 1">
</div>
<div class="item">
<img src="./imgs/positions/infra_3.jpg" alt="Infrastructure Tower 1">
</div>
<div class="item">
<img src="./imgs/positions/recording_1.jpg" alt="Recording Scene 1">
</div>
<div class="item">
<img src="./imgs/positions/calib.jpg" alt="Calibration per position">
</div>
<div class="item">
<img src="./imgs/positions/infra_1.jpg" alt="Infrastructure Tower 2">
</div>
<div class="item">
<img src="./imgs/positions/wn_1.jpg" alt="Record in Waiblingen">
</div>
<div class="item">
<img src="./imgs/positions/st_1.jpg" alt="Record in Stuttgart">
</div>
</div>
</div>
</div>
</section>
<section class="section">
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-3">Automatic Ground Truth</h2>
<div class="content has-text-justified">
<p>
<!-- Abstract. -->
Since we are a small lab with limited resources and labeling is cost-intensive, we explored scalable alternatives
through automated annotation and anonymization pipelines. Rather than focusing on traditional detection benchmarks,
CoopScenes emphasizes infrastructure-vehicle interoperability. Using mighty offline Transformer models like
<a href="https://github.com/Sense-X/Co-DETR" target="_blank">Co-DETR</a>,
we generated high-quality 2D instance labels, achieving an F1-score of 89.4% (try to beat that ;D) for vehicles (Note: The test set is meticulously
hand-labeled and may not align perfectly with the model’s output format). The dataset includes over 5.5 million
detected instances and will be extended with 3D labels and hand-annotated subsets to support benchmarking and
further model development.
</p>
</div>
<div class="content has-text-centered">
<img src="./imgs/reference_labels.png" alt="Reference-Labels">
</div>
</div>
</div>
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-3">Spatial Registration</h2>
<div class="content has-text-justified">
<p>
To enable sensor interoperation, all sensors are extrinsically calibrated relative to a designated agent origin—either
the vehicle's top sensor or the tower—reducing the problem to a single inter-agent transformation.
Using the tower as the global origin, we estimate the inter-agent transformation via LiDAR-based
<a href="https://github.com/PRBonn/kiss-icp" target="_blank">KISS-ICP</a>,
supported by
<a href="https://ieeexplore.ieee.org/document/5152473" target="_blank">Fast Point Feature Histograms (FPFH)</a>.
Initial alignment is selected from GNSS-proximal frames using
RANSAC and refined with ICP, then clustered with DBSCAN. Transformations are propagated, enabling estimates
even without direct sensor overlap. Reliable transformations are refined per frame; others are interpolated
from odometry, assuming a static tower.
</p>
</div>
<div class="content has-text-centered">
<img src="./imgs/projection.png" alt="Camera-LiDAR-Projection">
</div>
<div class="content has-text-justified">
<p>
Quantitative evaluation of cross-agent point cloud projections onto images is challenging; therefore, we focus
on qualitative assessments. As shown, even under challenging conditions—such as high speeds and distant
objects—the projections exhibit competitive alignment quality.
</p>
</div>
</div>
</div>
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-3">Anonymization</h2>
<div class="content has-text-justified">
<p>
We are pleased to present our dataset, with a strong emphasis on anonymization to ensure privacy protection.
Recent research has questioned whether object detectors trained on anonymized data perform comparably to those
trained on unaltered data. To address this concern, we developed BlurScene, a face and license plate detector
based on Faster R-CNN, available at
<a href="https://github.com/CoopScenes/BlurScene" target="_blank">github</a>.
Trained on publicly available datasets and evaluated on our labeled CoopScenes test set, we distinguished
between identifiable and non-identifiable instances to optimize the balance between privacy and utility.
Although the model prioritizes high recall, we selected an operating point optimized for precision to minimize
unnecessary blur.
</p>
</div>
<div class="content has-text-centered">
<img src="./imgs/BlurScene_unnessecaryBlur.png" alt="BlurScene-Curve">
</div>
<div class="content has-text-justified">
<p>
Currently, anonymization is applied using an adaptive mosaic filter for a more natural appearance. We are
actively exploring advanced anonymization through style transfer techniques such as
<a href="https://github.com/orpatashnik/StyleCLIP" target="_blank">StyleCLIP</a>,
<a href="https://junyanz.github.io/CycleGAN/" target="_blank">CycleGAN</a>,
and <a href="https://github.com/hukkelas/deep_privacy2" target="_blank">DeepPrivacy2</a>.
Until then, all code, data, and development strategies are open source—and we welcome contributions from the community.
</p>
</div>
</div>
</div>
</div>
</section>
<section class="section">
<div class="container is-max-desktop">
<!-- 3d-rgb. -->
<div class="columns is-centered">
<div class="column is-full-width">
<h2 class="title is-3">3D-RGB Pointclouds</h2>
<div class="content has-text-justified">
<p>
We offer 3D RGB point clouds generated by integrating data from LiDAR and cameras, providing detailed
spatial and color information for enhanced scene understanding.
</p>
</div>
<div class="content has-text-centered">
<img src="./imgs/3d_rgb_infra_pointcloud.png" alt="RGB Pointcloud from the Infratstructure">
</div>
<div class="content has-text-centered">
<img src="./imgs/3d_rgb_vehicle_pointcloud.png" alt="RGB Pointcloud from the Infratstructure">
</div>
</div>
</div>
<!-- Double Content -->
<div class="columns is-centered">
<!-- Infra -->
<div class="column">
<h2 class="title is-3">Camera-Lidar Projections</h2>
<div class="content has-text-justified">
<p>
A key component of the dataset is the viewpoint from the observing infrastructure, providing a comprehensive
perspective from the environment. Every color indicates another sensor.
</p>
<img src="imgs/VIEW_1_sensors.png" alt="Lidar-Camera projection from Infrastructure 1">
</div>
</div>
<!--/ Infra -->
<!-- Stereo -->
<div class="column">
<h2 class="title is-3">Stereo Disparity Maps</h2>
<div class="content has-text-justified">
<p>
We provide a high-quality stereo camera system designed for precise depth perception and 3D imaging
(front-facing camera only), all accessible through our research and development kit.
</p>
<img src="imgs/stereo_disp_view.png" alt="Disparity Image of the Vehicle Front Camera">
</div>
</div>
<!--/ Stereo -->
</div>
<!--/ Double Content -->
</div>
</section>
<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">BibTeX</h2>
<pre><code>@INPROCEEDINGS{11097591,
author = {Vosshans, Marcel and Baumann, Alexander and Drueppel, Matthias and Ait-Aider, Omar and Mezouar, Youcef and Dang, Thao and Enzweiler, Markus},
booktitle = {2025 IEEE Intelligent Vehicles Symposium (IV)},
title = {CoopScenes: Multi-Scene Infrastructure and Vehicle Data for Advancing Collective Perception in Autonomous Driving},
year = {2025},
pages = {1040-1047},
keywords = {Point cloud compression;Roads;Urban areas;Pipelines;Real-time systems;Information filtering;Synchronization;Intelligent systems;License plate recognition;Information integrity},
doi = {10.1109/IV64158.2025.11097591}}
</code></pre>
</div>
</section>
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<!-- Acknowledgement -->
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<div class="column is-8">
<div class="content">
<h2 class="title is-4">Acknowledgement</h2>
<p>Our work has been made possible through the support of the following partners, who provided essential
financial backing, industry expertise, and hands-on assistance:
</p>
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<a href="https://vm.baden-wuerttemberg.de/en/home" target="_blank">
<figure class="image" style="margin: 0 20px;">
<img src="static/images/ministry_of_transport_bw_logo.svg" alt="Ministry of Transportation">
</figure>
</a>
<a href="https://www.hs-esslingen.de/en" target="_blank">
<figure class="image" style="margin: 0 20px;">
<img src="static/images/hse_logo.svg" alt="Esslingen University">
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<a href="https://www.dhbw-stuttgart.de/en/" target="_blank">
<figure class="image" style="margin: 0 20px;">
<img src="static/images/dhbw_logo.svg" alt="DHBW Stuttgart">
</figure>
</a>
<a href="https://volkmann-sv.de/en/" target="_blank">
<figure class="image" style="margin: 0 20px;">
<img src="static/images/vsv_logo.svg" alt="Volkmann">
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</a>
<a href="https://company.softing.com/" target="_blank">
<figure class="image" style="margin: 0 20px;">
<img src="static/images/kaitos_logo.jpg" alt="Softing">
</figure>
</a>
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<p>
The template is borrowed from <a
href="https://github.com/nerfies/nerfies.github.io">Nerfies</a>
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