We recover a video of the motion taking place in a hidden scene by observing changes in indirect illumination in a nearby uncalibrated visible region. We solve this problem by factoring the observed video into a matrix product between the unknown hidden scene video and an unknown light transport matrix. This task is extremely ill-posed, as any non-negative factorization will satisfy the data. Inspired by recent work on the Deep Image Prior, we parameterize the factor matrices using randomly initialized convolutional neural networks trained in a one-off manner, and show that this results in decompositions that reflect the true motion in the hidden scene.
翻译:我们通过观察附近未校准可见区域的间接光化变化,在隐蔽场景中恢复了该运动的视频。 我们通过将观察到的视频纳入未知的隐藏场景视频和未知的轻运输矩阵之间的矩阵产品来解决这个问题。 这项任务极不理想, 因为任何非负乘法都会满足数据要求。 在《 深图像前》最近的工作启发下, 我们使用一次性训练的随机初始化的神经神经网络对要素矩阵进行参数化, 并表明这会导致反映隐藏场景真实运动的分解。