mlsanityData to 7 Oct 2026

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

arXiv 2309.15751 ↗First posted 27 Sep 2023cs.CVcited by 12

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Abstract (arXiv)

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 hrefhttps://ensta-u2is.github.io/infraParis/https://ensta-u2is.github.io/infraParis/.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 34 of its 51 references that are in the map. First posted on arXiv 27 Sep 2023.

Builds on

34 of 51 references.

References · 34

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22 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 4; 2026: 4.

12 papers.