# ViViD++: Vision for Visibility Dataset

arXiv: https://arxiv.org/abs/2204.06183 · HTML: https://mlsanity.com/paper/2204.06183/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 38 references that are in the map.
- First posted on arXiv 13 Apr 2022.

## Builds on (most cited first, 9 of 9)

- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [DSEC: A Stereo Event Camera Dataset for Driving Scenarios](https://mlsanity.com/paper/2103.06011/) (2021)
- [The Multi Vehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perception](https://mlsanity.com/paper/1801.10202/) (2018)
- [The Event-Camera Dataset and Simulator: Event-based Data for Pose Estimation, Visual Odometry, and SLAM](https://mlsanity.com/paper/1610.08336/) (2016)
- [Events-to-Video: Bringing Modern Computer Vision to Event Cameras](https://mlsanity.com/paper/1904.08298/) (2019)
- [InteriorNet: Mega-scale Multi-sensor Photo-realistic Indoor Scenes Dataset](https://mlsanity.com/paper/1809.00716/) (2018)
- [Event-based visual place recognition with ensembles of temporal windows](https://mlsanity.com/paper/2006.02826/) (2020)
- [A Photometrically Calibrated Benchmark For Monocular Visual Odometry](https://mlsanity.com/paper/1607.02555/) (2016)
- [Run Your Visual-Inertial Odometry on NVIDIA Jetson: Benchmark Tests on a Micro Aerial Vehicle](https://mlsanity.com/paper/2103.01655/) (2021)

## Built on by (most cited first, 30 of 34)

- [Deep Learning for Event-based Vision: A Comprehensive Survey and Benchmarks](https://mlsanity.com/paper/2302.08890/) (2023)
- [VECtor: A Versatile Event-Centric Benchmark for Multi-Sensor SLAM](https://mlsanity.com/paper/2207.01404/) (2022)
- [EVIMO2: An Event Camera Dataset for Motion Segmentation, Optical Flow, Structure from Motion, and Visual Inertial Odometry in Indoor Scenes with Monocular or Stereo Algorithms](https://mlsanity.com/paper/2205.03467/) (2022)
- [Emergent Visual Sensors for Autonomous Vehicles](https://mlsanity.com/paper/2205.09383/) (2022)
- [eCDT: Event Clustering for Simultaneous Feature Detection and Tracking-](https://mlsanity.com/paper/2207.09108/) (2022)
- [(LC)2: LiDAR-Camera Loop Constraints For Cross-Modal Place Recognition](https://mlsanity.com/paper/2304.08660/) (2023)
- [Implicit Event-RGBD Neural SLAM](https://mlsanity.com/paper/2311.11013/) (2023)
- [R-LiViT: A LiDAR-Visual-Thermal Dataset Enabling Vulnerable Road User Focused Roadside Perception](https://mlsanity.com/paper/2503.17122/) (2025)
- [Fieldscale: Locality-Aware Field-based Adaptive Rescaling for Thermal Infrared Image](https://mlsanity.com/paper/2405.15395/) (2024)
- [Event-Aided Time-to-Collision Estimation for Autonomous Driving](https://mlsanity.com/paper/2407.07324/) (2024)
- [FIReStereo: Forest InfraRed Stereo Dataset for UAS Depth Perception in Visually Degraded Environments](https://mlsanity.com/paper/2409.07715/) (2024)
- [Pixel-aligned RGB-NIR Stereo Imaging and Dataset for Robot Vision](https://mlsanity.com/paper/2411.18025/) (2024)
- [SuperEvent: Cross-Modal Learning of Event-based Keypoint Detection for SLAM](https://mlsanity.com/paper/2504.00139/) (2025)
- [An Asynchronous Intensity Representation for Framed and Event Video Sources](https://mlsanity.com/paper/2301.08783/) (2023)
- [VXP: Voxel-Cross-Pixel Large-scale Image-LiDAR Place Recognition](https://mlsanity.com/paper/2403.14594/) (2024)
- [MonoTher-Depth: Enhancing Thermal Depth Estimation via Confidence-Aware Distillation](https://mlsanity.com/paper/2504.16127/) (2025)
- [A Survey of 3D Reconstruction with Event Cameras](https://mlsanity.com/paper/2505.08438/) (2025)
- [AnyThermal: Towards Learning Universal Representations for Thermal Perception](https://mlsanity.com/paper/2602.06203/) (2026)
- [Unveiling the Depths: A Multi-Modal Fusion Framework for Challenging Scenarios](https://mlsanity.com/paper/2402.11826/) (2024)
- [Deep Depth Estimation from Thermal Image: Dataset, Benchmark, and Challenges](https://mlsanity.com/paper/2503.22060/) (2025)
- [TherA: Thermal-Aware Visual-Language Prompting for Controllable RGB-to-Thermal Infrared Translation](https://mlsanity.com/paper/2602.19430/) (2026)
- [Thermal Chameleon: Task-Adaptive Tone-mapping for Radiometric Thermal-Infrared images](https://mlsanity.com/paper/2410.18340/) (2024)
- [EvTTC: An Event Camera Dataset for Time-to-Collision Estimation](https://mlsanity.com/paper/2412.05053/) (2024)
- [SEAR: Simple and Efficient Adaptation of Visual Geometric Transformers for Unpaired RGB+Thermal 3D Reconstruction](https://mlsanity.com/paper/2603.18774/) (2026)
- [Galibr: Targetless LiDAR-Camera Extrinsic Calibration Method via Ground Plane Initialization](https://mlsanity.com/paper/2406.11599/) (2024)
- [RGB-Thermal Infrared Fusion for Robust Depth Estimation in Complex Environments](https://mlsanity.com/paper/2503.04821/) (2025)
- [MULTIAQUA: A multimodal maritime dataset and robust training strategies for multimodal semantic segmentation](https://mlsanity.com/paper/2512.17450/) (2025)
- [All-day Depth Completion via Thermal-LiDAR Fusion](https://mlsanity.com/paper/2504.02356/) (2025)
- [Thegra: Graph-based SLAM for Thermal Imagery](https://mlsanity.com/paper/2602.08531/) (2026)
- [Thermal Image Refinement with Depth Estimation using Recurrent Networks for Monocular ORB-SLAM3](https://mlsanity.com/paper/2603.14998/) (2026)

## Abstract (arXiv)

In this paper, we present a dataset capturing diverse visual data formats that target varying luminance conditions. While RGB cameras provide nourishing and intuitive information, changes in lighting conditions potentially result in catastrophic failure for robotic applications based on vision sensors. Approaches overcoming illumination problems have included developing more robust algorithms or other types of visual sensors, such as thermal and event cameras. Despite the alternative sensors' potential, there still are few datasets with alternative vision sensors. Thus, we provided a dataset recorded from alternative vision sensors, by handheld or mounted on a car, repeatedly in the same space but in different conditions. We aim to acquire visible information from co-aligned alternative vision sensors. Our sensor system collects data more independently from visible light intensity by measuring the amount of infrared dissipation, depth by structured reflection, and instantaneous temporal changes in luminance. We provide these measurements along with inertial sensors and ground-truth for developing robust visual SLAM under poor illumination. The full dataset is available at: https://visibilitydataset.github.io/