Non-line-of-sight[NLOS]imaging is an emerging technique for detecting objects behind obstacles or around corners.Recent studies on passive NLOS mainly focus on steady-state measurement and reconstruction methods,which...Non-line-of-sight[NLOS]imaging is an emerging technique for detecting objects behind obstacles or around corners.Recent studies on passive NLOS mainly focus on steady-state measurement and reconstruction methods,which show limitations in recognition of moving targets.To the best of our knowledge,we propose a novel event-based passive NLOS imaging method.We acquire asynchronous event-based data of the diffusion spot on the relay surface,which contains detailed dynamic information of the NLOS target,and efficiently ease the degradation caused by target movement.In addition,we demonstrate the event-based cues based on the derivation of an event-NLOS forward model.Furthermore,we propose the first event-based NLOS imaging data set,EM-NLOS,and the movement feature is extracted by time-surface representation.We compare the reconstructions through event-based data with frame-based data.The event-based method performs well on peak signal-to-noise ratio and learned perceptual image patch similarity,which is 20%and 10%better than the frame-based method.展开更多
基金supported by the National Natural Science Foundation of China(No.62031018)。
文摘Non-line-of-sight[NLOS]imaging is an emerging technique for detecting objects behind obstacles or around corners.Recent studies on passive NLOS mainly focus on steady-state measurement and reconstruction methods,which show limitations in recognition of moving targets.To the best of our knowledge,we propose a novel event-based passive NLOS imaging method.We acquire asynchronous event-based data of the diffusion spot on the relay surface,which contains detailed dynamic information of the NLOS target,and efficiently ease the degradation caused by target movement.In addition,we demonstrate the event-based cues based on the derivation of an event-NLOS forward model.Furthermore,we propose the first event-based NLOS imaging data set,EM-NLOS,and the movement feature is extracted by time-surface representation.We compare the reconstructions through event-based data with frame-based data.The event-based method performs well on peak signal-to-noise ratio and learned perceptual image patch similarity,which is 20%and 10%better than the frame-based method.
