There is rising interest in differentiable rendering, which allows explicitly modeling geometric priors and constraints in optimization pipelines using first-order methods such as backpropagation. Incorporating such domain knowledge 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. 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.

翻译：可微分渲染技术日益受到关注，该方法允许在优化流程中利用反向传播等一阶方法显式建模几何先验与约束。通过融入此类领域知识，可训练出鲁棒性更强且所需数据更少的深度神经网络，同时具备求解病态逆问题的能力。现有可微分渲染研究主要聚焦于光电传感器图像，特别是传统RGB图像。本文提出一种针对合成孔径雷达（SAR）图像的可微分渲染方法，该技术融合了三维计算机图形学与神经渲染技术。我们以高保真模拟SAR数据为基础，通过有限SAR图像实现三维物体重建这一逆向图形学问题验证了该方法的有效性。