Paper Detail

PriSAR: 3D Geometric-Prior-Guided Diffusion for Parameter-Controlled SAR Image Generation

Fan Zhang, Xuanting Wu, Fei Ma, Qiang Yin, Yuxin Hu

arxiv Score 4.3

Published 2026-07-25 · First seen 2026-07-28

General AI

Abstract

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.

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BibTeX

@article{zhang2026prisar,
  title = {PriSAR: 3D Geometric-Prior-Guided Diffusion for Parameter-Controlled SAR Image Generation},
  author = {Fan Zhang and Xuanting Wu and Fei Ma and Qiang Yin and Yuxin Hu},
  year = {2026},
  abstract = {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},
  url = {https://arxiv.org/abs/2607.22963},
  keywords = {eess.IV, cs.CV},
  eprint = {2607.22963},
  archiveprefix = {arXiv},
}

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