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.

翻译：非视距（NLOS）成像技术因其在救援行动、自动驾驶汽车行人检测等领域的广泛应用潜力而日益受到关注。然而，在移动摄像机上实现NLOS成像仍是一个开放性研究领域。现有NLOS成像方法依赖时间分辨探测器与激光配置系统，其精密光学对准要求导致难以在动态环境中部署。本研究提出一种数据驱动的NLOS成像方法PathFinder，该方法可适配安装于小型功率受限移动机器人（如空中无人机）的标准RGB相机。实验流程设计用于精确估计在曼哈顿世界环境中移动但始终处于相机视场外的人体二维轨迹。我们创新性地采用基于注意力机制的神经网络，对视线（LOS）视频中的连续动态帧序列进行实时推理处理。该方法还包含预处理选择指标，可分析移动相机拍摄的包含多个垂直平面（如墙壁与建筑外立面）的图像，并提取提供最大NLOS信息的平面。通过无人机拍摄的野外场景验证表明，该方法可在动态拍摄环境中实现低成本NLOS成像。