ViViD++: Vision for Visibility Dataset
This provides a dataset for researchers in robotics and computer vision to develop more robust vision-based applications under challenging lighting conditions, but it is incremental as it builds on existing sensor types without introducing new methods.
The authors tackled the lack of datasets for alternative vision sensors by presenting ViViD++, a dataset capturing diverse visual data formats under varying luminance conditions, including infrared, depth, and event-based measurements, along with inertial sensors and ground-truth for robust visual SLAM in poor illumination.
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/