The extraction of a clean background image by removing foreground occlusion holds immense practical significance, but it also presents several challenges. Presently, the majority of de-occlusion research focuses on addressing this issue through the extraction and synthesis of discrete images from calibrated camera arrays. Nonetheless, the restoration quality tends to suffer when faced with dense occlusions or high-speed motions due to limited perspectives and motion blur. To successfully remove dense foreground occlusion, an effective multi-view visual information integration approach is required. Introducing the spike camera as a novel type of neuromorphic sensor offers promising capabilities with its ultra-high temporal resolution and high dynamic range. In this paper, we propose an innovative solution for tackling the de-occlusion problem through continuous multi-view imaging using only one spike camera without any prior knowledge of camera intrinsic parameters and camera poses. By rapidly moving the spike camera, we continually capture the dense stream of spikes from the occluded scene. To process the spikes, we build a novel model \textbf{SpkOccNet}, in which we integrate information of spikes from continuous viewpoints within multi-windows, and propose a novel cross-view mutual attention mechanism for effective fusion and refinement. In addition, we contribute the first real-world spike-based dataset \textbf{S-OCC} for occlusion removal. The experimental results demonstrate that our proposed model efficiently removes dense occlusions in diverse scenes while exhibiting strong generalization.

翻译：从密集连续视图中移除前景遮挡以提取干净背景图像具有重要的实际意义，但也面临诸多挑战。当前，大多数去遮挡研究通过从标定相机阵列中提取和合成离散图像来解决该问题。然而，由于视角有限和运动模糊，在密集遮挡或高速运动场景下，恢复质量往往不尽人意。要成功移除密集前景遮挡，需要有效的多视角视觉信息融合方法。引入脉冲相机作为新型神经形态传感器，凭借其超高时间分辨率和高动态范围展现出巨大潜力。本文提出一种创新解决方案，仅使用单台脉冲相机在无需任何相机内参和位姿先验知识的情况下，通过连续多视角成像解决去遮挡问题。通过快速移动脉冲相机，我们连续采集遮挡场景的密集脉冲流。为处理脉冲数据，我们构建了新型模型 \textbf{SpkOccNet}，该模型在多窗口内整合连续视角的脉冲信息，并提出一种新颖的跨视图互注意力机制用于有效融合与优化。此外，我们贡献了首个用于遮挡移除的真实脉冲数据集 \textbf{S-OCC}。实验结果表明，所提模型能在多种场景中高效移除密集遮挡，并展现出强大的泛化能力。