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Conference Papers Year : 2024

InfraParis: A multi-modal and multi-task autonomous driving dataset


Current deep neural networks (DNNs) for autonomous driving computer vision are typically trained on specific datasets that only involve a single type of data and urban scenes. Consequently, these models struggle to handle new objects, noise, nighttime conditions, and diverse scenarios, which is essential for safety-critical applications. Despite ongoing efforts to enhance the resilience of computer vision DNNs, progress has been sluggish, partly due to the absence of benchmarks featuring multiple modalities. We introduce a novel and versatile dataset named InfraParis that supports multiple tasks across three modalities: RGB, depth, and infrared. We assess various state-of-the-art baseline techniques, encompassing models for the tasks of semantic segmentation, object detection, and depth estimation. More visualizations and the download link for InfraParis are available at

Dates and versions

hal-04321062 , version 1 (04-12-2023)





Gianni Franchi, Marwane Hariat, Xuanlong Yu, Nacim Belkhir, Antoine Manzanera, et al.. InfraParis: A multi-modal and multi-task autonomous driving dataset. WACV 2024 - IEEE/CVF Winter Conference on Applications of Computer Vision, Jan 2024, Waikoloa, United States. ⟨hal-04321062⟩
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