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<title>InfraParis: A multi-modal and multi-task autonomous driving dataset</title>
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<h2>InfraParis Dataset</h2><br/><h4><strong>A multi-modal and multi-task autonomous driving dataset</strong></h4>
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<h2>InfraParis Dataset</h2><br/><h4><strong>7,301 annotated images with different modalities and tasks. </strong></h4>
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<h2>InfraParis Dataset</h2><br/><h4><strong>4 supported computer vision tasks</strong></h4>
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<h2>Overview</h2>
<h6>InfraParis: A multi-modal and multi-task autonomous driving dataset</h6>
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<h3><font size="5">InfraParis</font></h3>
<p>We present InfraParis, an extensive dataset designed for autonomous driving applications. This dataset is characterized by its multimodal and multitasking nature, encompassing a total of 7,301 data samples. Specifically, it consists of 6,567 images in the training set, 189 in the validation set, and 571 in the test set. InfraParis is structured to support various tasks across three distinct modalities: RGB, depth, and infrared. These tasks encompass object detection, semantic segmentation, and depth prediction.</p>
<p><b>InfraParis encompasses four distinct tasks, each catering to different aspects of autonomous driving:</b></p>
<ul class="about-us-list">
<li class="points2"><b>Semantic Segmentation Task</b>: This task involves images with associated semantic segmentation labels.</li>
<li class="points2"><b>Object Detection Task</b>: In this task, the dataset provides images with bounding boxes delineating pedestrians.</li>
<li class="points2"><b>Supervised Depth Prediction Task</b>: For this task, the dataset includes images with ground truth depth information.</li>
<li class="points2"><b>Unsupervised Depth Prediction Task</b>: Here, the dataset comprises sequences of images along with ground truth depth data.</li>
</ul>
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<p><b>InfraParis has 4 modalities:</b></p>
<ul class="about-us-list">
<li class="points2"><b>RGB Images</b></li>
<li class="points2"><b>Infrared Images</b></li>
<li class="points2"><b>RGB Video</b></li>
<li class="points2"><b>Depth Images </b></li>
</ul>
<p><b>Overview of annotated classes:</b></p>
<p> There are a total of 155 fine-grained classes, which are also aggregated to facilitate the use along with other datasets, e.g. Citiscapes:</p>
<!-- <table><tr><td>Cityscapes classes</td><td>MUAD classes</td><td>nb. of images with the annotations</td></tr><tr><td>Road</td><td>Bots, Tram Tracks, Crosswalk, Parking Area, Garbage - Road, Road Lines, Sewer Longitudinal Crack, Transversal Crack, Road, Asphalt hole, Polished Aggregate, Vegetation - Road, Sewer - Road, Construction Concrete</td><td>9055</td></tr><tr><td>Sidewalk</td><td>Lane Bike, Kerb Stone, Sidewalk, Kerb Rising Edge</td><td>8948</td></tr><tr><td>Building</td><td>House, Construction Scaffold, Building, Air Conditioning, Construction Container, TV Antenna, Terrace, Water Tank, Pergola Garden, Stairs, Dog House, Sunshades, Railings, Construction Stock, Marquees, Hangar Airport</td><td>9089</td></tr><tr><td>Wall</td><td>Wall</td><td>1101</td></tr><tr><td>Fence</td><td>Construction Fence, Fences</td><td>8622</td></tr><tr><td>Pole</td><td>Traffic Signs Poles or Structure, Traffic Lights Poles, Street lights, Lamp</td><td>8984</td></tr><tr><td>Traffic light</td><td>Traffic Lights Head, Traffic Cameras, Traffic Lights Bulb (red, yellow, green)</td><td>8222</td></tr><tr><td>Traffic sign</td><td>Traffic Signs</td><td>2672</td></tr><tr><td>Vegetation</td><td>Vegetation</td><td>9072</td></tr><tr><td>Terrain</td><td>Terrain, Tree Pit</td><td>8377</td></tr><tr><td>Sky</td><td>Sky</td><td>8591</td></tr><tr><td>Person</td><td>Walker, All colors of Construction Helmet, All colors of Safety Vest, Umbrella, People</td><td>8843</td></tr><tr><td>Rider</td><td>Cyclist, Biker</td><td>3470</td></tr><tr><td>Car</td><td>Car, Beacon Light, Van, Ego Car</td><td>9026</td></tr><tr><td>Truck</td><td>Truck</td><td>5533</td></tr><tr><td>Bus</td><td>Bus</td><td>0</td></tr><tr><td>Train</td><td>Train, Subway</td><td>2240</td></tr><tr><td>Motorcycle</td><td>Motorcycle, Segway, Scooter Child</td><td>2615</td></tr><tr><td>Bicycle</td><td>Bicycle, Kickbike, Tricycle</td><td>2816</td></tr><tr><td>Animals</td><td>Cow, Bear, Deer, Moose</td><td>603</td></tr><tr><td>Objects anomalies</td><td>Stand food, Trash can, garbage bag</td><td>352</td></tr><tr><td>Background</td><td>Others</td><td>-</td></tr></table> -->
<table><tr><td>Cityscapes classes</td><td>InfraParis</td><td>nb. of images with the annotations</td></tr><tr><td>Road</td><td>Bots, Tram Tracks, Crosswalk, Parking Area, Garbage - Road, Road Lines, Sewer Longitudinal Crack, Transversal Crack, Road, Asphalt Hole, Polished Aggregate, Vegetation - Road, Sewer - Road, Construction Concrete</td><td>9055</td></tr><tr><td>Sidewalk</td><td>Lane Bike, Kerb Stone, Sidewalk, Kerb Rising Edge</td><td>8948</td></tr><tr><td>Building</td><td>House, Construction Scaffold, Building, Air Conditioning, Construction Container, TV Antenna, Terrace, Water Tank, Pergola Garden, Stairs, Dog House, Sunshades, Railings, Construction Stock, Marquees, Hangar Airport</td><td>9089</td></tr><tr><td>Wall</td><td>Wall</td><td>1101</td></tr><tr><td>Fence</td><td>Construction Fence, Fences</td><td>8622</td></tr><tr><td>Pole</td><td>Traffic Signs Poles or Structure, Traffic Lights Poles, Street lights, Lamp</td><td>8984</td></tr><tr><td>Traffic light</td><td>Traffic Lights Head, Traffic Cameras, Traffic Lights Bulb (red, yellow, green)</td><td>8222</td></tr><tr><td>Traffic sign</td><td>Traffic Signs</td><td>2672</td></tr><tr><td>Vegetation</td><td>Vegetation</td><td>9072</td></tr><tr><td>Terrain</td><td>Terrain, Tree Pit</td><td>8377</td></tr><tr><td>Sky</td><td>Sky</td><td>8591</td></tr><tr><td>Person</td><td>Walker, All colors of Construction Helmet, All colors of Safety Vest, Umbrella, People</td><td>8843</td></tr><tr><td>Rider</td><td>Cyclist, Biker</td><td>3470</td></tr><tr><td>Car</td><td>Car, Beacon Light, Van, Ego Car</td><td>9026</td></tr><tr><td>Truck</td><td>Truck</td><td>5533</td></tr><tr><td>Bus</td><td>Bus</td><td>0</td></tr><tr><td>Train</td><td>Train, Subway</td><td>2240</td></tr><tr><td>Motorcycle</td><td>Motorcycle, Segway, Scooter Child</td><td>2615</td></tr><tr><td>Bicycle</td><td>Bicycle, Kickbike, Tricycle</td><td>2816</td></tr><tr><td>Animals</td><td>Cow, Bear, Deer, Moose</td><td>603</td></tr><tr><td>Objects anomalies</td><td>Stand Food, Trash Can, Garbage bag</td><td>352</td></tr><tr><td>Background</td><td>Others</td><td>-</td></tr></table>
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<h3><center><font size="5">Terms of use</font></center></h3>
<br>
<font size="3">Copyright for InfraParis Dataset is owned by ENSTA Paris (U2IS Laboratory, Palaiseau, FR).
<br>
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<b> By downloading the dataset, you acknowledge and agree to abide by the terms outlined in the InfraParis release agreement. </b>
<b> If you need InfraParis Dataset, please Click and Fill in this <a href="https://docs.google.com/forms/d/e/1FAIpQLSe2kzVsfD3657AlppTInV8Yp1ptWEic6_Nl5VisNoHu9nTYvQ/viewform?usp=sf_link">Google form.</a> </b>
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<b>[Note] We will release all the test sets (with the RGB images and the ground truth maps) after the InfraParis challenge on the Codalab. Currently, only a small part of the test sets is released with only the RGB images.</b>
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<h3><center><font size="5">Acknowledgments</font></center></h3>
<br>
<font size="3">We gratefully acknowledge the support of AID Project ACoCaTherm which supported the creation of the dataset. We are also grateful to Remi KAZMIERCZAK and Adrien LAFAGE
for their help with the early processing of the dataset, as well as the many staff who worked hard to annotate the dataset. </font>
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<font size="3">If you use InfraParis in your work, please cite this publication:</font>
<ul class="about-us-list">
<br>
<li class="points"><b>@inproceedings{franchi2023infraparis,</b></li>
<li class="points"><b> title={InfraParis: A multi-modal and multi-task autonomous driving dataset},</b></li>
<li class="points"><b> author={Franchi, Gianni and Hariat, Marwane and Yu, Xuanlong and Belkhir, Nacim and Manzanera, Antoine and Filliat, David},</b></li>
<li class="points"><b> booktitle={WACV 2024},</b></li>
<!--<li class="points"><b> publisher={{BMVA} Press},</b></li>-->
<li class="points"><b> year={2024},</b></li>
<li class="points"><b> url={https://arxiv.org/abs/2309.15751}</b></li>
<li class="points"><b>}</b></li>
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<h3 class="animated fadeInUp wow">Gianni Franchi</h3>
<p class="animated fadeInDown wow">U2IS, ENSTA Paris, Institut Polytechnique de Paris</p>
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<h3 class="animated fadeInUp wow">Marwane Hariat</h3>
<p class="animated fadeInDown wow">U2IS, ENSTA Paris, Institut Polytechnique de Paris</p>
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