  Multi-baseline SAR 3D imaging faces significant challenges due to data
sparsity. In recent years, deep learning techniques have achieved notable
success in enhancing the quality of sparse SAR 3D imaging. However, previous
work typically rely on full-aperture high-resolution radar images to supervise
the training of deep neural networks (DNNs), utilizing only single-modal
information from radar data. Consequently, imaging performance is limited, and
acquiring full-aperture data for multi-baseline SAR is costly and sometimes
impractical in real-world applications. In this paper, we propose a Cross-Modal
Reconstruction Network (CMR-Net), which integrates differentiable render and
cross-modal supervision with optical images to reconstruct highly sparse
multi-baseline SAR 3D images of vehicle targets into visually structured and
high-resolution images. We meticulously designed the network architecture and
training strategies to enhance network generalization capability. Remarkably,
CMR-Net, trained solely on simulated data, demonstrates high-resolution
reconstruction capabilities on both publicly available simulation datasets and
real measured datasets, outperforming traditional sparse reconstruction
algorithms based on compressed sensing and other learning-based methods.
Additionally, using optical images as supervision provides a cost-effective way
to build training datasets, reducing the difficulty of method dissemination.
Our work showcases the broad prospects of deep learning in multi-baseline SAR
3D imaging and offers a novel path for researching radar imaging based on
cross-modal learning theory.
