ViViD++: Vision for Visibility Dataset

TL;DR

This paper introduces ViViD++, a comprehensive dataset with diverse alternative vision sensors capturing visual data under varying lighting conditions to improve robotic vision robustness in poor illumination.

Contribution

The paper provides a new dataset with multi-sensor data, including infrared, depth, and temporal luminance changes, for developing robust visual SLAM in challenging lighting.

Findings

Dataset includes synchronized data from multiple sensors.

Enables research on vision under poor illumination.

Supports development of illumination-invariant algorithms.

Abstract

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/