R1A-PC：复杂静态环境中三维人体点云的物理引导电磁反演

中文摘要 AI 辅助

在复杂静态环境中从电磁测量中恢复三维人体几何形状是困难的，因为来自墙壁、地板和其他物体的强多径响应掩盖了弱目标扰动。我们提出R1A-PC，一种物理引导方法，从有无目标时测量的成对复场中重建2048点人体点云。复背景减法强调目标引起的幅度和相位变化，而背景场仍作为环境条件可用。频率平衡的离散Born伴随算子生成三维空间知识图。在两个有界变形阶段中的每一个阶段，解码器结合复测量特征、背景特征以及在当前点坐标处查询的多尺度物理特征；第二阶段在第一次坐标更新后再次查询。我们分析了成对减法的残差、原始伴随算子的加权法向算子结构以及预测点云的可行集。在固定采集几何下全波模拟生成的留出背景中，R1A-PC获得平方Chamfer距离0.001434平方米和F分数0.963080（在0.05米处）。与TopNet相比，Chamfer距离减少了70.28%。移除物理引导或背景减法分别使Chamfer距离增加242.06%或241.18%。跨背景布局和姿态的实验支持成对减法和位置相关伴随特征的互补作用。

英文摘要

Recovering three-dimensional human geometry from electromagnetic measure?ments in a complex static environment is difficult because strong multipath responses from walls, floors, and other objects obscure the weak target per?turbation. We propose R1A-PC, a physics-guided method that reconstructs a 2048-point human cloud from paired complex fields measured with and without the target. Complex background subtraction emphasizes target-induced ampli?tude and phase changes, while the background field remains available as an environmental condition. A frequency-balanced discrete Born adjoint produces a three-dimensional spatial knowledge map. At each of two bounded deformation stages, the decoder combines complex measurement features, background fea?tures, and multiscale physical features queried at the current point coordinates; the second stage queries again after the first coordinate update. We analyze the residual of paired subtraction, the weighted normal-operator structure of the raw adjoint, and the feasible set of the predicted cloud. In a held-out background generated by full-wave simulation under a fixed acquisition geometry, R1A-PC obtains a squared Chamfer distance of 0.001434 m2 and an F-score of 0.963080 at 0.05 m. Compared with TopNet, the Chamfer distance decreases by 70.28%. Removing physical guidance or background subtraction increases the Chamfer distance by 242.06% or 241.18%, respectively. Experiments across background layouts and poses support the complementary roles of paired subtraction and position-dependent adjoint features.

发表机构

• Changchun University of Science and Technology（长春理工大学）

机构由 AI 辅助整理，请以论文原文为准。