标题： 用于射频光线追踪的神经反射场 显示英文标题

标题： Neural Reflectance Fields for Radio-Frequency Ray Tracing

摘要： 光线追踪被广泛用于模拟射频（RF）信号在复杂环境中的传播。 建模性能在很大程度上取决于目标场景的描述准确性，包括场景几何形状和表面材料特性。 计算机视觉和LiDAR的发展使场景几何估计越来越准确，但在现实环境中仍缺乏可扩展且高效的材料反射率估计方法。 在本工作中，我们通过从发射器到接收器的射频信号路径损耗中高效学习材料反射率来解决这个问题。 具体来说，我们希望学习到的材料反射系数能够最小化接收器预测功率与测量功率之间的差距。 我们通过将神经反射场从光学领域转换到射频领域来实现这一目标，通过建模射频信号的幅度和相位来考虑多路径效应。 我们进一步提出了一种可微分的射频光线追踪框架，该框架优化神经反射场以匹配信号强度测量结果。 我们为实验模拟了一个复杂的现实环境，我们的仿真结果表明，神经反射场可以成功学习所有入射角度的反射系数。 因此，与现有方法相比，我们的方法在使用显著更少训练数据的情况下，能够更准确地预测接收器的功率。 显示英文摘要

摘要： Ray tracing is widely employed to model the propagation of radio-frequency (RF) signal in complex environment. The modelling performance greatly depends on how accurately the target scene can be depicted, including the scene geometry and surface material properties. The advances in computer vision and LiDAR make scene geometry estimation increasingly accurate, but there still lacks scalable and efficient approaches to estimate the material reflectivity in real-world environment. In this work, we tackle this problem by learning the material reflectivity efficiently from the path loss of the RF signal from the transmitters to receivers. Specifically, we want the learned material reflection coefficients to minimize the gap between the predicted and measured powers of the receivers. We achieve this by translating the neural reflectance field from optics to RF domain by modelling both the amplitude and phase of RF signals to account for the multipath effects. We further propose a differentiable RF ray tracing framework that optimizes the neural reflectance field to match the signal strength measurements. We simulate a complex real-world environment for experiments and our simulation results show that the neural reflectance field can successfully learn the reflection coefficients for all incident angles. As a result, our approach achieves better accuracy in predicting the powers of receivers with significantly less training data compared to existing approaches.