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.

本文提出了一种使用合成孔径雷达图像进行可微分渲染的方法，结合了 3D 计算机图形学和神经渲染，并在高保真模拟 SAR 数据上演示了从有限 SAR 图像重建 3D 对象的逆图形问题。