Non-line-of-sight (NLOS) imaging has the ability to recover 3D images of scenes outside the direct line of sight, which is of growing interest for diverse applications. Despite the remarkable progress, NLOS imaging of dynamic objects is still challenging. It requires a large amount of multibounce photons for the reconstruction of single frame data. To overcome this obstacle, we develop a computational framework for dynamic time-of-flight NLOS imaging based on plug-and-play (PnP) algorithms. By combining imaging forward model with the deep denoising network from the computer vision community, we show a 4 frames-per-second (fps) 3D NLOS video recovery (128 × 128 × 512) in post processing. Our method leverages the temporal similarity among adjacent frames and incorporates sparse priors and frequency filtering. This enables higher-quality reconstructions for complex scenes. Extensive experiments are conducted to verify the superior performance of our proposed algorithm both through simulations and real data.

非视距 (NLOS) 成像能够恢复直接视线之外场景的 3D 图像，这在各种应用中越来越受到关注。尽管取得了显着的进展，动态物体的非视距成像仍然具有挑战性。它需要大量的多次反射光子来重建单帧数据。为了克服这一障碍，我们开发了一种基于即插即用（PnP）算法的动态飞行时间非视距成像计算框架。通过将图像前向模型与计算机视觉社区的深度去噪网络相结合，我们在后处理中展示了每秒 4 帧 (fps) 的 3D NLOS 视频恢复 (128 × 128 × 512)。我们的方法利用相邻帧之间的时间相似性，并结合稀疏先验和频率过滤。这使得复杂场景的重建质量更高。通过模拟和真实数据进行了大量的实验来验证我们提出的算法的优越性能。