The study of non-line-of-sight (NLOS) imaging is growing due to its many
potential applications, including rescue operations and pedestrian detection by
self-driving cars. However, implementing NLOS imaging on a moving camera
remains an open area of research. Existing NLOS imaging methods rely on
time-resolved detectors and laser configurations that require precise optical
alignment, making it difficult to deploy them in dynamic environments. This
work proposes a data-driven approach to NLOS imaging, PathFinder, that can be
used with a standard RGB camera mounted on a small, power-constrained mobile
robot, such as an aerial drone. Our experimental pipeline is designed to
accurately estimate the 2D trajectory of a person who moves in a
Manhattan-world environment while remaining hidden from the camera's
field-of-view. We introduce a novel approach to process a sequence of dynamic
successive frames in a line-of-sight (LOS) video using an attention-based
neural network that performs inference in real-time. The method also includes a
preprocessing selection metric that analyzes images from a moving camera which
contain multiple vertical planar surfaces, such as walls and building facades,
and extracts planes that return maximum NLOS information. We validate the
approach on in-the-wild scenes using a drone for video capture, thus
demonstrating low-cost NLOS imaging in dynamic capture environments.

非直视成像是一个活跃的研究领域，在动态环境中基于移动相机实现非直视成像一直是一个未解决的问题。本研究提出了一种数据驱动的非直视成像方法，PathFinder，可以通过在小型、功耗受限的移动机器人上安装标准 RGB 相机来使用。我们使用一个基于注意力机制的神经网络来处理连续帧序列的线视 ideo，从中提取包含多个垂直平面的图像，并选择提取获得最大非直视成像信息的平面。我们使用无人机进行视频采集，并验证了该方法在野外场景中的有效性，从而实现了低成本的动态非直视成像。

路径探索者：基于注意力的动态非直视追踪与移动机器人

PathFinder: Attention-Driven Dynamic Non-Line-of-Sight Tracking with a  Mobile Robot

Motion magnification helps us visualize subtle, imperceptible motion.
However, prior methods only work for 2D videos captured with a fixed camera. We
present a 3D motion magnification method that can magnify subtle motions from
scenes captured by a moving camera, while supporting novel view rendering. We
represent the scene with time-varying radiance fields and leverage the Eulerian
principle for motion magnification to extract and amplify the variation of the
embedding of a fixed point over time. We study and validate our proposed
principle for 3D motion magnification using both implicit and tri-plane-based
radiance fields as our underlying 3D scene representation. We evaluate the
effectiveness of our method on both synthetic and real-world scenes captured
under various camera setups.

运用 Euler 原理提取和放大固定点随时间嵌入变化的 3D 运动放大方法，支持运动相机拍摄场景的变化、创新视角渲染。通过时间变化的辐射场来表示场景，并在隐含和三平面辐射场作为三维场景表示中验证所提出的 3D 运动放大原理的有效性。在各种摄像机设置下，我们评估了所提出方法在合成和真实世界场景中的有效性。

3D 运动增强：利用时间变化辐射场可视化微小运动

3D Motion Magnification: Visualizing Subtle Motions with Time Varying  Radiance Fields

We present a learning-based approach for removing unwanted obstructions, such
as window reflections, fence occlusions, or adherent raindrops, from a short
sequence of images captured by a moving camera. Our method leverages motion
differences between the background and obstructing elements to recover both
layers. Specifically, we alternate between estimating dense optical flow fields
of the two layers and reconstructing each layer from the flow-warped images via
a deep convolutional neural network. This learning-based layer reconstruction
module facilitates accommodating potential errors in the flow estimation and
brittle assumptions, such as brightness consistency. We show that the proposed
approach learned from synthetically generated data performs well to real
images. Experimental results on numerous challenging scenarios of reflection
and fence removal demonstrate the effectiveness of the proposed method.

我们提出了一种基于学习的方法来消除从移动相机拍摄的短序列图像中的不需要的干扰物，例如窗户反射，栅栏遮挡或附着的雨滴。我们的方法利用背景和阻挡元素之间的运动差异来恢复两个图层，并在估计两个层的密集光流场和通过深度卷积神经网络从流场变形的图像重建每个层之间进行交替。通过学习的重建模块有助于适应流量估计和脆弱假设（例如亮度一致性）中的潜在错误。我们显示，该方法在从合成数据中学习后对真实图像具有很好的表现。在许多具有挑战性的反射和围栏移除情况下进行的实验结果证明了该方法的有效性。