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

arXiv: https://arxiv.org/abs/2309.15751 · HTML: https://mlsanity.com/paper/2309.15751/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 51 references that are in the map.
- First posted on arXiv 27 Sep 2023.

## Builds on (most cited first, 30 of 34)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning](https://mlsanity.com/paper/1805.04687/) (2018)
- [Depth Map Prediction from a Single Image using a Multi-Scale Deep Network](https://mlsanity.com/paper/1406.2283/) (2014)
- [Playing for Data: Ground Truth from Computer Games](https://mlsanity.com/paper/1608.02192/) (2016)
- [Argoverse: 3D Tracking and Forecasting with Rich Maps](https://mlsanity.com/paper/1911.02620/) (2019)
- [Semantic Foggy Scene Understanding with Synthetic Data](https://mlsanity.com/paper/1708.07819/) (2017)
- [Virtual Worlds as Proxy for Multi-Object Tracking Analysis](https://mlsanity.com/paper/1605.06457/) (2016)
- [One Thousand and One Hours: Self-driving Motion Prediction Dataset](https://mlsanity.com/paper/2006.14480/) (2020)
- [IDD: A Dataset for Exploring Problems of Autonomous Navigation in Unconstrained Environments](https://mlsanity.com/paper/1811.10200/) (2018)
- [SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation](https://mlsanity.com/paper/2104.14812/) (2021)
- [SHIFT: A Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptation](https://mlsanity.com/paper/2206.08367/) (2022)
- [Dark Model Adaptation: Semantic Image Segmentation from Daytime to Nighttime](https://mlsanity.com/paper/1810.02575/) (2018)
- [Lost and Found: Detecting Small Road Hazards for Self-Driving Vehicles](https://mlsanity.com/paper/1609.04653/) (2016)
- [Toward Driving Scene Understanding: A Dataset for Learning Driver Behavior and Causal Reasoning](https://mlsanity.com/paper/1811.02307/) (2018)
- [Rain rendering for evaluating and improving robustness to bad weather](https://mlsanity.com/paper/2009.03683/) (2020)
- [HeatNet: Bridging the Day-Night Domain Gap in Semantic Segmentation with Thermal Images](https://mlsanity.com/paper/2003.04645/) (2020)
- [Curriculum Model Adaptation with Synthetic and Real Data for Semantic Foggy Scene Understanding](https://mlsanity.com/paper/1901.01415/) (2019)
- [Segmenting Objects in Day and Night:Edge-Conditioned CNN for Thermal Image Semantic Segmentation](https://mlsanity.com/paper/1907.10303/) (2019)
- [PTB-TIR: A Thermal Infrared Pedestrian Tracking Benchmark](https://mlsanity.com/paper/1801.05944/) (2018)

## Built on by (most cited first, 12 of 12)

- [MUSES: The Multi-Sensor Semantic Perception Dataset for Driving under Uncertainty](https://mlsanity.com/paper/2401.12761/) (2024)
- [Dataset and Benchmark: Novel Sensors for Autonomous Vehicle Perception](https://mlsanity.com/paper/2401.13853/) (2024)
- [Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation](https://mlsanity.com/paper/2411.17610/) (2024)
- [SAGA: Semantic-Aware Gray color Augmentation for Visible-to-Thermal Domain Adaptation across Multi-View Drone and Ground-Based Vision Systems](https://mlsanity.com/paper/2504.15728/) (2025)
- [RASMD: RGB And SWIR Multispectral Driving Dataset for Robust Perception in Adverse Conditions](https://mlsanity.com/paper/2504.07603/) (2025)
- [Learning to Match Unpaired Data with Minimum Entropy Coupling](https://mlsanity.com/paper/2503.08501/) (2025)
- [IndraEye: Infrared Electro-Optical UAV-based Perception Dataset for Robust Downstream Tasks](https://mlsanity.com/paper/2410.20953/) (2024)
- [Depth-Sensitive Soft Suppression with RGB-D Inter-Modal Stylization Flow for Domain Generalization Semantic Segmentation](https://mlsanity.com/paper/2505.07050/) (2025)
- [No Calibration, No Depth, No Problem: Cross-Sensor View Synthesis with 3D Consistency](https://mlsanity.com/paper/2602.23559/) (2026)
- [Unified Restoration-Perception Learning: Maritime Infrared-Visible Image Fusion and Segmentation](https://mlsanity.com/paper/2603.28414/) (2026)
- [Towards All-Day Perception for Off-Road Driving: A Large-Scale Multispectral Dataset and Comprehensive Benchmark](https://mlsanity.com/paper/2604.27499/) (2026)
- [Descriptor: LYNRED Mobility Dataset Multimodal Detection Subset (LYNRED-MDS)](https://mlsanity.com/paper/2607.01871/) (2026)

## 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/.