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.

利用卷积神经网络参数化分解方法，从相邻未标定可见区域间的间接光照变化中恢复出隐藏场景中的运动轨迹。

计算镜面：通过深度矩阵分解实现盲反向光传输

Computational Mirrors: Blind Inverse Light Transport by Deep Matrix  Factorization

Vehicles, search and rescue personnel, and endoscopes use flash lights to
locate, identify, and view objects in their surroundings. Here we show the
first steps of how all these tasks can be done around corners with consumer
cameras. Recent techniques for NLOS imaging using consumer cameras have not
been able to both localize and identify the hidden object. We introduce a
method that couples traditional geometric understanding and data-driven
techniques. To avoid the limitation of large dataset gathering, we train the
data-driven models on rendered samples to computationally recover the hidden
scene on real data. The method has three independent operating modes: 1) a
regression output to localize a hidden object in 2D, 2) an identification
output to identify the object type or pose, and 3) a generative network to
reconstruct the hidden scene from a new viewpoint. The method is able to
localize 12cm wide hidden objects in 2D with 1.7cm accuracy. The method also
identifies the hidden object class with 87.7% accuracy (compared to 33.3%
random accuracy). This paper also provides an analysis on the distribution of
information that encodes the occluded object in the accessible scene. We show
that, unlike previously thought, the area that extends beyond the corner is
essential for accurate object localization and identification.

本研究使用消费级相机，通过结合传统几何理解和数据驱动技术，提出一种新的方法，实现了在拐角处定位和识别目标物体；该方法还能够用于在新视点生成被遮挡的场景，并能够通过分析信息分布，精确定位和识别超出拐角范围的物体。