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.
InfraParis encompasses four distinct tasks, each catering to different aspects of autonomous driving:
- Semantic Segmentation Task with associated semantic segmentation labels.
- Object Detection Task with bounding boxes delineating pedestrians.
- Supervised Depth Prediction Task with ground truth depth information.
- Unsupervised Depth Prediction Task with sequences of images along with ground truth depth data.
Gianni Franchi (U2IS, ENSTA Paris, Institut Polytechnique de Paris) Marwane Hariat (U2IS, ENSTA Paris, Institut Polytechnique de Paris) Xuanlong Yu (SATIE, Paris Saclay University U2IS, ENSTA Paris, Institut Polytechnique de Paris) Nacim Belkhir (SafranTech) Antoine Manzanera (U2IS, ENSTA Paris, Institut Polytechnique de Paris) David Filliat (U2IS, ENSTA Paris, Institut Polytechnique de Paris)
If you have any questions (about the dataset or this website), please feel free to contact (replace at by @):
- Gianni Franchi gianni.franchi at ensta-paris.fr
- Marwane Hariat marwane.hariat at ensta-paris.fr
- Xuanlong Yu xuanlong.yu at ensta-paris.fr
Franchi, G., Hariat, M., Yu, X., Belkhir, N., Manzanera, A., & Filliat, D. (2024). InfraParis: A multi-modal and multi-task autonomous driving dataset. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (pp. 2973-2983).
@inproceedings{franchi2024infraparis,
title={InfraParis: A multi-modal and multi-task autonomous driving dataset},
author={Franchi, Gianni and Hariat, Marwane and Yu, Xuanlong and Belkhir, Nacim and Manzanera, Antoine and Filliat, David},
booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
pages={2973--2983},
year={2024}
}```