PCFlow：用于GPR B扫描图像合成的物理条件流匹配

中文摘要 AI 辅助

探地雷达（GPR）B扫描图像合成对于数据增强、算法验证和仿真加速具有重要意义，然而生成既具有视觉真实感又具有物理一致性的雷达图仍然具有挑战性。现有的基于学习的生成模型通常强调视觉外观，但对响应几何形状的控制有限。在本文中，我们提出了PCFlow，一种用于快速GPR B扫描图像合成的物理条件流匹配框架。PCFlow的核心是一个由用于电磁仿真的参数化物理模型构建的麦克斯韦知情密集物理条件场，包括材料属性、目标几何形状、传播线索和响应域先验。该条件场提供了物理场景参数与雷达响应几何之间的可解释接口，并引导VAE潜空间中的条件流匹配走向物理上可行的生成路径。我们在基于gprMax的埋地管道数据集上评估了PCFlow，包括分布内和分布外测试用例。实验结果表明，PCFlow生成的图像具有更准确的响应几何形状和高视觉保真度，证明了其在可控且物理保真的雷达图像合成方面的有效性。

英文摘要

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.

发表机构

• College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）
• Symbol Computation and Knowledge Engineering of the Ministry of Education, Jilin University（吉林大学符号计算与知识工程教育部重点实验室）

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