Differentiable Rendering for Synthetic Aperture Radar Imagery
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There is rising interest in integrating signal and image processing pipelines into deep learning training to incorporate more domain knowledge. This can lead to deep neural networks that are trained more robustly and with limited data, as well as the capability to solve ill-posed inverse problems. In particular, there is rising interest in differentiable rendering, which allows explicitly modeling geometric priors and constraints in the optimization pipeline using first-order methods such as backpropagation. Existing efforts in differentiable rendering have focused on imagery from electro-optical sensors, particularly conventional RGB-imagery. In this work, we propose an approach for differentiable rendering of Synthetic Aperture Radar (SAR) imagery, which combines methods from 3D computer graphics with neural rendering. We demonstrate the approach on the inverse graphics problem of 3D Object Reconstruction from limited SAR imagery using high-fidelity simulated SAR data.
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