Conventional imaging requires a line of sight to create accurate visual representations of a scene. In certain circumstances, however, obtaining a suitable line of sight may be impractical, dangerous, or even impossible. Non-line-of-sight (NLOS) imaging addresses this challenge by reconstructing the scene from indirect measurements. Recently, passive NLOS methods that use an ordinary photograph of the subtle shadow cast onto a visible wall by the hidden scene have gained interest. These methods are currently limited to 1D or low-resolution 2D color imaging or to localizing a hidden object whose shape is approximately known. Here, we generalize this class of methods and demonstrate a 3D reconstruction of a hidden scene from an ordinary NLOS photograph. To achieve this, we propose a novel reformulation of the light transport model that conveniently decomposes the hidden scene into \textit{light-occluding} and \textit{non-light-occluding} components to yield a separable non-linear least squares (SNLLS) inverse problem. We develop two solutions: A gradient-based optimization method and a physics-inspired neural network approach, which we call Soft Shadow diffusion (SSD). Despite the challenging ill-conditioned inverse problem encountered here, our approaches are effective on numerous 3D scenes in real experimental scenarios. Moreover, SSD is trained in simulation but generalizes well to unseen classes in simulation and real-world NLOS scenes. SSD also shows surprising robustness to noise and ambient illumination.

翻译：传统成像需要视线来创建场景的精确视觉表示。然而在某些情况下，获得合适的视线可能不切实际、危险甚至不可能。非视距（NLOS）成像通过间接测量重建场景来应对这一挑战。最近，利用隐藏场景在可见墙面上投射的细微阴影的普通照片进行被动NLOS成像的方法引起了广泛关注。这些方法目前仅限于一维或低分辨率二维彩色成像，或局限于定位形状大致已知的隐藏物体。本文推广了此类方法，并实现了从普通NLOS照片对隐藏场景的三维重建。为此，我们提出了一种新颖的光传输模型重构方法，将隐藏场景便捷地分解为\textit{光遮挡}与\textit{非光遮挡}分量，从而形成可分离非线性最小二乘（SNLLS）逆问题。我们开发了两种解决方案：基于梯度的优化方法和物理启发式神经网络方法，后者被称为软影扩散（SSD）。尽管面临极具挑战性的病态逆问题，我们的方法在真实实验场景中的多个三维场景上均表现出有效性。此外，SSD在仿真环境中训练，却能很好地泛化到仿真和真实世界NLOS场景中的未见类别。SSD还展现出对噪声和环境光照的显著鲁棒性。