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The extraction of a clean background image by removing foreground occlusionholds immense practical significance, but it also presents several challenges.Presently, the majority of de-occlusion research focuses on addressing thisissue through the extraction and synthesis of discrete images from calibratedcamera arrays. Nonetheless, the restoration quality tends to suffer when facedwith dense occlusions or high-speed motions due to limited perspectives andmotion blur. To successfully remove dense foreground occlusion, an effectivemulti-view visual information integration approach is required. Introducing thespike camera as a novel type of neuromorphic sensor offers promisingcapabilities with its ultra-high temporal resolution and high dynamic range. Inthis paper, we propose an innovative solution for tackling the de-occlusionproblem through continuous multi-view imaging using only one spike camerawithout any prior knowledge of camera intrinsic parameters and camera poses. Byrapidly moving the spike camera, we continually capture the dense stream ofspikes from the occluded scene. To process the spikes, we build a novel model\textbf{SpkOccNet}, in which we integrate information of spikes from continuousviewpoints within multi-windows, and propose a novel cross-view mutualattention mechanism for effective fusion and refinement. In addition, wecontribute the first real-world spike-based dataset \textbf{S-OCC} forocclusion removal. The experimental results demonstrate that our proposed modelefficiently removes dense occlusions in diverse scenes while exhibiting stronggeneralization.

Unveiling the Potential of Spike Streams for Foreground Occlusion Removal from Densely Continuous Views