Drones have revolutionized the fields of aerial imaging, mapping, and disaster recovery. However, the deployment of drones in low-light conditions is constrained by the image quality produced by their on-board cameras. In this paper, we present a learning architecture for improving 3D reconstructions in low-light conditions by finding features in a burst. Our approach enhances visual reconstruction by detecting and describing high quality true features and less spurious features in low signal-to-noise ratio images. We demonstrate that our method is capable of handling challenging scenes in millilux illumination, making it a significant step towards drones operating at night and in extremely low-light applications such as underground mining and search and rescue operations.

翻译：无人机已彻底改变了航空成像、测绘和灾后恢复领域。然而，无人机在弱光条件下的部署受到其机载相机成像质量的限制。本文提出一种学习架构，通过从突发图像序列中提取特征来改善弱光条件下的三维重建。我们的方法通过检测和描述低信噪比图像中的高质量真实特征并减少伪特征，从而增强视觉重建效果。我们证明，该方法能够处理毫勒克斯照度下的挑战性场景，这标志着无人机在夜间及地下采矿、搜救等极弱光应用中的运行能力迈出了重要一步。