Abstract
Non-line-of-sight (NLOS) imaging of objects not visible to either the camera or illumination source is a challenging task with vital applications including surveillance and robotics. Recent NLOS reconstruction advances have been achieved using time-resolved measurements which requires expensive and specialized detectors and laser sources. In contrast, we propose a data-driven approach for NLOS 3D localization requiring only a conventional camera and projector. We achieve an average identification of 79% object identification for three classes of objects, and localization of the NLOS object's centroid for a mean-squared error (MSE) of 2.89cm in the occluded region for real data taken from a hardware prototype. To generalize to line-of-sight (LOS) scenes with non-planar surfaces, we introduce an adaptive lighting algorithm. This algorithm, based on radiosity, identifies and illuminates scene patches in the LOS which most contribute to the NLOS light paths, and can factor in system power constraints. We further improve our average NLOS object identification to 87.8% accuracy and localization to 1.94cm MSE on a complex LOS scene using adaptive lighting for real data, demonstrating the advantage of combining the physics of light transport with active illumination for data-driven NLOS imaging.
Abstract (translated)
对于摄像机或照明源都看不到的物体的非视距成像（NLOS）是一项具有挑战性的任务，具有重要的应用，包括监视和机器人技术。最近的非直瞄重建进展是通过时间分辨测量来实现的，这需要昂贵的专用探测器和激光源。相比之下，我们提出了一种非直瞄三维定位的数据驱动方法，只需要一个传统的摄像机和投影仪。我们实现了三类物体79%的平均识别，以及非直瞄物体的质心定位，平均平方误差（mse）为2.89cm，在封闭区域内，用于从硬件原型中获取真实数据。为了推广具有非平面表面的视距（LOS）场景，我们引入了一种自适应照明算法。该算法以光能传递为基础，对视距中对非直瞄光路贡献最大的场景块进行识别和照明，并考虑系统功率约束。我们进一步将非直瞄目标的平均识别精度提高到87.8%，并在复杂的视距场景中使用自适应照明进行实时数据定位，从而证明了将光传输物理与主动照明相结合用于数据驱动非直瞄成像的优势。
URL
https://arxiv.org/abs/1905.11595
