PCFlow: Physics-Conditioned Flow Matching for GPR B-Scan Image Synthesis

2026-09-07

AI Summary

为解决GPR B-扫描图像生成中视觉逼真度与物理一致性的难题，提出PCFlow方法，通过物理条件流匹配框架指导VAE潜在空间中的生成路径。

Abstract

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.

Problem

• Ground-penetrating radar
• B-scan image synthesis
• Physical consistency
• Visual realism

Innovation

• Physics-Conditioned Flow Matching
• Dense Physical Condition Field
• Maxwell-Informed
• VAE Latent Space
• Physically Faithful Synthesis