发表机构
College of Information Science and Technology, Beijing University of Chemical Technology; Aerospace Information Research Institute, Chinese Academy of Sciences（北京化工大学信息科学与技术学院; 中国科学院空天信息研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究SAR图像在稀疏观测角度下的可控生成问题，提出用3D模型导出的几何先验引导扩散模型的方法，即GeoDiff-SAR，经实验在飞机和车辆数据集上取得较好结果，证明该轻量级3D几何先验可改善视点一致性，用作生成指导。
AI 中文摘要
合成孔径雷达（SAR）图像生成可缓解数据稀缺问题，但在稀疏观测角度下进行可控生成仍很困难。近期SAR生成研究改善了纹理逼真度，但明确的几何感知控制仍有限。本文研究中间方位角补全的重点且可验证设置：3D模型导出的几何先验引导扩散模型合成稀疏角度训练数据中缺失的视图。GeoDiff-SAR构建轻量级多反射光线追踪先验，编码所得点云，并在通过低秩适应调整Stable Diffusion 3.5 Medium时将其与文本条件融合。在真实的四类飞机数据集上，GeoDiff-SAR的结构相似性指数（SSIM）达到0.812，方位一致性达到0.940，而文本条件的SD3.5 Medium基线分别为0.738和0.782。在五个MSTAR车辆类别上使用相同的稀疏角度协议，得到的SSIM为0.878，方位一致性为0.917。这些结果支持了轻量级3D几何先验可改善可控SAR生成的视点一致性的结论；其旨在作为生成指导而非高保真电磁重建。
英文摘要
Synthetic aperture radar (SAR) image generation can mitigate data scarcity, but controllablegeneration under sparse observation angles remains difficult. Recent SAR generative studies im-prove texture realism, yet explicit geometry-aware control is still limited. This paper studiesthe focused and verifiable setting of intermediate-azimuth completion: 3D-model-derived geo-metric priors guide a diffusion model to synthesize the views missing from sparse-angle trainingdata. GeoDiff-SAR constructs a lightweight multi-bounce ray-tracing prior, encodes the result-ing point cloud, and fuses it with text conditioning while adapting Stable Diffusion 3.5 Mediumthrough low-rank adaptation. On a real four-category aircraft dataset, GeoDiff-SAR reaches anSSIM of 0.812 and azimuth consistency of 0.940, compared with 0.738 and 0.782 for the text-conditioned SD3.5 Medium baseline. The same sparse-angle protocol on five MSTAR vehicleclasses yields an SSIM of 0.878 and azimuth consistency of 0.917. These results support theconclusion that a lightweight 3D geometric prior improves viewpoint adherence for controllableSAR generation; it is intended as generation guidance rather than high-fidelity electromagneticreconstruction.
Comments17 pages,15 images
