论文标题
合成孔径雷达图像的可区分渲染
Differentiable Rendering for Synthetic Aperture Radar Imagery
论文作者
论文摘要
对可区分渲染的兴趣不大，它允许使用一阶方法（例如反向传播）在优化管道中明确建模几何学先验和约束。结合此类领域知识可以导致深层的神经网络，这些神经网络受到更强和有限的数据的训练，以及解决不良反问题的能力。现有的可区分渲染工作集中在电流传感器（尤其是常规RGB模拟物）的图像上。在这项工作中，我们提出了一种方法，用于可区分合成孔径雷达（SAR）图像的方法，该方法将3D计算机图形的方法与神经渲染相结合。我们使用高保真模拟的SAR数据证明了有限SAR成像的3D对象重建的反相图形问题的方法。
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.
