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。