用逆渲染方法从任意视角的雷达图像重建三维地形，突破传统干涉测量限制。
Multi-view 3D surface reconstruction from SAR images by inverse rendering
- 基于可微分雷达渲染模型，从高程图和后向散射系数图合成SAR图像。
- 通过粗到精策略，仅用少数SAR视图即可拟合出精确的表面高度与外观。
- 适用于无严格成像条件约束的多源数据融合，适合遥感与地形建模领域。
从合成孔径雷达（SAR）图像进行三维场景重建通常依赖干涉测量，对采集过程有严格要求。近年来，深度学习推动了光学成像中多视角三维重建的发展，主要基于神经辐射场等重建-合成方法。本文提出一种新的逆渲染方法，用于从非受限SAR图像中进行三维重建，借鉴光学方法思路。首先，构建一个简化的可微分SAR渲染模型，能从数字高程模型和雷达后向散射系数图合成SAR图像；其次，采用粗到精策略，训练一个多层感知机（MLP）从少量SAR视图中拟合目标雷达场景的高度与外观；最后，我们在ONERA的物理基EMPRISE模拟器生成的合成SAR图像上验证了该方法的表面重建能力。结果表明，该方法有效利用了SAR图像中的几何差异，为多传感器数据融合开辟了新路径。
原文摘要 · Abstract (English)
3D reconstruction of a scene from Synthetic Aperture Radar (SAR) images mainly relies on interferometric measurements, which involve strict constraints on the acquisition process. These last years, progress in deep learning has significantly advanced 3D reconstruction from multiple views in optical imaging, mainly through reconstruction-by-synthesis approaches pioneered by Neural Radiance Fields. In this paper, we propose a new inverse rendering method for 3D reconstruction from unconstrained SAR images, drawing inspiration from optical approaches. First, we introduce a new simplified differentiable SAR rendering model, able to synthesize images from a digital elevation model and a radar backscattering coefficients map. Then, we introduce a coarse-to-fine strategy to train a Multi-Layer Perceptron (MLP) to fit the height and appearance of a given radar scene from a few SAR views. Finally, we demonstrate the surface reconstruction capabilities of our method on synthetic SAR images produced by ONERA's physically-based EMPRISE simulator. Our method showcases the potential of exploiting geometric disparities in SAR images and paves the way for multi-sensor data fusion.
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