PathFinder：基于注意力驱动的移动机器人动态非视距跟踪
PathFinder: Attention-Driven Dynamic Non-Line-of-Sight Tracking with a Mobile Robot
AI总结：
PathFinder提出一种基于注意力神经网络的数据驱动方法，使搭载标准RGB相机的小型移动机器人能够实时估计视场外行人的二维轨迹，并通过平面选择度量在动态环境中实现低成本非视距成像。
摘要：
非视距（NLOS）成像的研究正在不断发展，因为它在救援行动和自动驾驶汽车行人检测等领域具有许多潜在应用。然而，在移动相机上实现NLOS成像仍然是一个开放的研究领域。现有的NLOS成像方法依赖于时间分辨探测器和需要精确光学对准的激光配置，这使得在动态环境中部署它们变得困难。本工作提出了一种数据驱动的NLOS成像方法PathFinder，该方法可与安装在小型、功率受限的移动机器人（如空中无人机）上的标准RGB相机配合使用。我们的实验流程旨在准确估计在曼哈顿世界环境中移动、同时保持在相机视场之外隐藏状态的人员的二维轨迹。我们引入了一种新颖的方法，利用基于注意力的神经网络对视线（LOS）视频中的动态连续帧序列进行处理，并实现实时推理。该方法还包括一个预处理选择度量，用于分析来自移动相机的图像，这些图像包含多个垂直平面表面（如墙壁和建筑立面），并提取返回最大NLOS信息的平面。我们使用无人机进行视频采集，在野外场景中验证了该方法，从而证明了在动态采集环境中实现低成本NLOS成像的可行性。
英文摘要：
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.
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