用物理模型控制生成雷达图像，让仿真更真实、可控。
PCFlow: Physics-Conditioned Flow Matching for GPR B-Scan Image Synthesis
- 基于电磁物理模型构建条件场，指导生成过程。
- 生成图像几何更准，视觉质量高，支持分布内外测试。
- 适合需要物理一致性的雷达仿真与数据增强场景。
地下探测雷达（GPR）B-scan 图像合成对数据增强、算法验证和仿真加速至关重要，但同时保证视觉真实性和物理一致性仍具挑战。现有学习型生成模型多关注外观，难以控制回波几何结构。本文提出 PCFlow，一种基于物理约束的流匹配框架，用于快速生成 GPR B-scan 图像。其核心是一个由参数化电磁仿真模型构建的麦克斯韦启发的密集物理条件场，包含材料属性、目标几何、传播线索及响应域先验。该条件场在物理场景参数与雷达响应几何间提供可解释接口，引导变分自编码器（VAE）潜空间中的条件流匹配，实现物理可行的生成路径。我们在 gprMax 基础的埋设管道数据集上进行评估，涵盖分布内与分布外测试案例。结果表明，PCFlow 生成的图像在响应几何精度和视觉保真度方面均更优，验证了其在可控且物理一致雷达图像合成中的有效性。
原文摘要 · Abstract (English)
Ground-penetrating radar (GPR) B-scan image synthesis is important for data augmentation, algorithm validation, and simulation acceleration, yet generating radargrams with both visual realism and physical consistency remains challenging. Existing learning-based generative models often emphasize visual appearance but provide limited control over response geometry. In this paper, we propose PCFlow, a physics-conditioned flow matching framework for fast GPR B-scan image synthesis. The core of PCFlow is a Maxwell-informed dense physical condition field constructed from the parameterized physical model used for electromagnetic simulation, including material properties, target geometry, propagation cues, and response-domain priors. This condition field provides an interpretable interface between physical scene parameters and radar response geometry, and guides conditional flow matching in the VAE latent space toward physically feasible generation paths. We evaluate PCFlow on a gprMax-based buried-pipeline dataset with both in-distribution and out-of-distribution test cases. Experimental results show that PCFlow generates images with more accurate response geometry and high visual fidelity, demonstrating its effectiveness for controllable and physically faithful radar image synthesis.
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