Unwanted camera occlusions, such as debris, dust, rain-drops, and snow, can
severely degrade the performance of computer-vision systems. Dynamic occlusions
are particularly challenging because of the continuously changing pattern.
Existing occlusion-removal methods currently use synthetic aperture imaging or
image inpainting. However, they face issues with dynamic occlusions as these
require multiple viewpoints or user-generated masks to hallucinate the
background intensity. We propose a novel approach to reconstruct the background
from a single viewpoint in the presence of dynamic occlusions. Our solution
relies for the first time on the combination of a traditional camera with an
event camera. When an occlusion moves across a background image, it causes
intensity changes that trigger events. These events provide additional
information on the relative intensity changes between foreground and background
at a high temporal resolution, enabling a truer reconstruction of the
background content. We present the first large-scale dataset consisting of
synchronized images and event sequences to evaluate our approach. We show that
our method outperforms image inpainting methods by 3dB in terms of PSNR on our
dataset.

通过结合传统相机和事件相机，本文提出了一种在动态遮挡存在的情况下从单个视点重建背景的新方法，利用遮挡引起的事件提供高时间分辨率的相对强度变化信息，实现对背景内容的更准确重建。通过大规模数据集的实验证明，我们的方法在图像修复方面比图像修复方法的 PSNR 提高了 3dB。