论文标题
生动++：可见性数据集的愿景
ViViD++: Vision for Visibility Dataset
论文作者
论文摘要
在本文中，我们提出一个数据集，该数据集捕获针对不同亮度条件的各种视觉数据格式。尽管RGB摄像机提供滋养和直观的信息，但照明条件的变化可能导致基于视觉传感器的机器人应用导致灾难性故障。克服照明问题的方法包括开发更强大的算法或其他类型的视觉传感器，例如热摄像机。尽管具有替代传感器的潜力，但仍然很少有具有替代视觉传感器的数据集。因此，我们提供了一个从替代视觉传感器，手持或安装在汽车上的数据集，在同一空间中反复但在不同的条件下。我们的目标是从共对象的替代视觉传感器中获取可见信息。我们的传感器系统通过测量红外耗散，通过结构化反射和亮度的瞬时时间变化来更独立于可见光强度。我们提供这些测量以及惯性传感器和地面真实性，以在照明不良的情况下发展出强大的视觉猛击。完整数据集可在以下网址提供：https：//visibilitydataset.github.io/
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/
