3D reconstruction techniques such as LiDAR scanning and photogrammetry have made it practical to build detailed geometric models of real-world environments. Such reconstructed models can potentially serve as the foundation for wireless digital twins and support network planning and optimization. The core challenge is that reconstructed models inevitably contain geometric artifacts such as holes and noisy surfaces, and wireless simulation is highly sensitive to such noise. To solve this problem, we propose a differentiable directional scattering model to approximate the noise-sensitive specular reflection. This approximation smoothly distributes reflected power among nearby ray directions, making the simulator inherently robust to local geometric artifacts in the reconstructed model. We prove mathematically that this approximation preserves asymptotic path-gain accuracy. Building on this idea, we propose mmDiff, an end-to-end differentiable framework for calibrating material properties from sparse mmWave measurements and predicting mmWave channels. We evaluate mmDiff on both real-world and synthetic datasets, and demonstrate its superior performance over prior methods using pure specular reflection in noisy reconstructed geometry.

翻译：诸如激光雷达扫描和摄影测量等三维重建技术，已使构建真实世界环境的详细几何模型成为可能。此类重建模型有望成为无线数字孪生的基础，并支持网络规划与优化。核心挑战在于，重建模型不可避免会包含孔洞、表面噪声等几何伪影，而无线仿真对此类噪声高度敏感。为解决该问题，我们提出一种可微分定向散射模型，用于近似对噪声敏感的镜面反射。该近似方法将反射能量平滑地分布至邻近射线方向，使模拟器天然具备对重建模型中局部几何伪影的鲁棒性。我们从数学上证明，该近似能保持路径增益的渐近精度。基于这一思想，我们提出mmDiff——一个端到端可微分框架，用于从稀疏毫米波测量中校准材料属性并预测毫米波信道。我们在真实数据集与合成数据集上对mmDiff进行评测，结果表明，在含噪重建几何场景中，其性能优于使用纯镜面反射的先前方法。