This paper presents a novel optimization-based method for non-line-of-sight
(NLOS) imaging that aims to reconstruct hidden scenes under various setups. Our
method is built upon the observation that photons returning from each point in
hidden volumes can be independently computed if the interactions between hidden
surfaces are trivially ignored. We model the generalized light propagation
function to accurately represent the transients as a linear combination of
these functions. Moreover, our proposed method includes a domain reduction
procedure to exclude empty areas of the hidden volumes from the set of
propagation functions, thereby improving computational efficiency of the
optimization. We demonstrate the effectiveness of the method in various NLOS
scenarios, including non-planar relay wall, sparse scanning patterns, confocal
and non-confocal, and surface geometry reconstruction. Experiments conducted on
both synthetic and real-world data clearly support the superiority and the
efficiency of the proposed method in general NLOS scenarios.

本研究提出了一种基于优化的方法，用于非直视成像，旨在重建不同设置下的隐藏场景。我们的方法建立在一个观察基础之上，即如果忽略隐藏表面之间的相互作用，从隐藏体积的每个点返回的光子可以独立计算。我们的方法利用广义光传播函数准确地表示瞬态，作为这些函数的线性组合。此外，我们提出的方法包括一个域缩减过程，将隐藏体积的空白区域从传播函数集合中排除，从而提高了优化的计算效率。我们在各种非直视场景中证明了该方法的有效性，包括非平面中继墙，稀疏扫描模式，共焦和非共焦，以及表面几何重建。对合成和真实数据进行的实验证明了该方法在一般非直视场景中的优越性和高效性。

非直视成像领域缩减策略

Domain Reduction Strategy for Non Line of Sight Imaging

The light transport matrix (LTM) is an instrumental tool in line-of-sight
(LOS) imaging, describing how light interacts with the scene and enabling
applications such as relighting or separation of illumination components. We
introduce a framework to estimate the LTM of non-line-of-sight (NLOS)
scenarios, coupling recent virtual forward light propagation models for NLOS
imaging with the LOS light transport equation. We design computational
projector-camera setups, and use these virtual imaging systems to estimate the
transport matrix of hidden scenes. We introduce the specific illumination
functions to compute the different elements of the matrix, overcoming the
challenging wide-aperture conditions of NLOS setups. Our NLOS light transport
matrix allows us to (re)illuminate specific locations of a hidden scene, and
separate direct, first-order indirect, and higher-order indirect illumination
of complex cluttered hidden scenes, similar to existing LOS techniques.

该论文提出了一种新的框架，使用虚拟成像系统来估算非直视成像场景下的光传输矩阵（Light transport matrix），克服了非直视成像装置的开口角限制，并且通过该光传输矩阵可以实现对隐蔽场景中某些特定位置（indirect illumination）的再照明以及直接、一阶和更高阶间接光照明（illumination）的分离，类似于现有的直视成像技术。