Passive non-line-of-sight imaging methods are often faster and stealthier
than their active counterparts, requiring less complex and costly equipment.
However, many of these methods exploit motion of an occluder or the hidden
scene, or require knowledge or calibration of complicated occluders. The edge
of a wall is a known and ubiquitous occluding structure that may be used as an
aperture to image the region hidden behind it. Light from around the corner is
cast onto the floor forming a fan-like penumbra rather than a sharp shadow.
Subtle variations in the penumbra contain a remarkable amount of information
about the hidden scene. Previous work has leveraged the vertical nature of the
edge to demonstrate 1D (in angle measured around the corner) reconstructions of
moving and stationary hidden scenery from as little as a single photograph of
the penumbra. In this work, we introduce a second reconstruction dimension:
range measured from the edge. We derive a new forward model, accounting for
radial falloff, and propose two inversion algorithms to form 2D reconstructions
from a single photograph of the penumbra. Performances of both algorithms are
demonstrated on experimental data corresponding to several different hidden
scene configurations. A Cramer-Rao bound analysis further demonstrates the
feasibility (and utility) of the 2D corner camera.

该论文介绍了利用墙壁边缘光晕成像的 Passive non-line-of-sight 方法，提出了一个新的 2D 重建模型和两种算法，并且通过实验数据说明了算法的性能和实用性。

从单个边缘遮挡物估计二维非直视场景

Two-Dimensional Non-Line-of-Sight Scene Estimation from a Single Edge  Occluder

A user-centric method for fast, interactive, robust and high-quality shadow
removal is presented. Our algorithm can perform detection and removal in a
range of difficult cases: such as highly textured and colored shadows. To
perform detection an on-the-fly learning approach is adopted guided by two
rough user inputs for the pixels of the shadow and the lit area. After
detection, shadow removal is performed by registering the penumbra to a
normalized frame which allows us efficient estimation of non-uniform shadow
illumination changes, resulting in accurate and robust removal. Another major
contribution of this work is the first validated and multi-scene category
ground truth for shadow removal algorithms. This data set containing 186 images
eliminates inconsistencies between shadow and shadow-free images and provides a
range of different shadow types such as soft, textured, colored and broken
shadow. Using this data, the most thorough comparison of state-of-the-art
shadow removal methods to date is performed, showing our proposed new algorithm
to outperform the state-of-the-art across several measures and shadow category.
To complement our dataset, an online shadow removal benchmark website is also
presented to encourage future open comparisons in this challenging field of
research.

提出一种以用户为中心的快速、交互式、强大和高质量的阴影去除方法，采用基于动态学习的检测方法及前沿用户指导的笔记，并采用归一化框架来实现阴影去除。此外，该论文还提供了第一个验证的和多场景类别的真实数据集，以及阴影去除算法的最全面比较和在线数据集的平台。