Neural surface reconstruction (NSR) has recently shown strong potential for urban 3D reconstruction from multi-view aerial imagery. However, existing NSR methods often suffer from geometric ambiguity and instability, particularly under sparse-view conditions. This issue is critical in large-scale urban remote sensing, where aerial image acquisition is limited by flight paths, terrain, and cost. To address this challenge, we present the first urban NSR framework that fuses 3D synthetic aperture radar (SAR) point clouds with aerial imagery for high-fidelity reconstruction under constrained, sparse-view settings. 3D SAR can efficiently capture large-scale geometry even from a single side-looking flight path, providing robust priors that complement photometric cues from images. Our framework integrates radar-derived spatial constraints into an SDF-based NSR backbone, guiding structure-aware ray selection and adaptive sampling for stable and efficient optimization. We also construct the first benchmark dataset with co-registered 3D SAR point clouds and aerial imagery, facilitating systematic evaluation of cross-modal 3D reconstruction. Extensive experiments show that incorporating 3D SAR markedly enhances reconstruction accuracy, completeness, and robustness compared with single-modality baselines under highly sparse and oblique-view conditions, highlighting a viable route toward scalable high-fidelity urban reconstruction with advanced airborne and spaceborne optical-SAR sensing.

翻译：神经表面重建（NSR）近期在基于多视角航拍影像的城市三维重建中展现出巨大潜力。然而，现有NSR方法常受几何模糊性和不稳定性困扰，在稀疏视角条件下尤为突出。这一问题在大规模城市遥感中至关重要，因为航拍影像的获取受飞行路径、地形和成本限制。为应对这一挑战，我们提出了首个融合三维合成孔径雷达（SAR）点云与航拍影像的城市NSR框架，用于在受限稀疏视角条件下实现高保真重建。三维SAR即使通过单次侧视飞行路径也能高效捕获大范围几何结构，为影像的光度线索提供稳健的补充先验。本框架将雷达衍生的空间约束集成到基于SDF的NSR主干网络中，通过引导结构感知的光线选择与自适应采样，实现稳定高效的优化。我们还构建了首个包含配准三维SAR点云与航拍影像的基准数据集，以促进跨模态三维重建的系统性评估。大量实验表明，在高度稀疏和倾斜视角条件下，引入三维SAR相比单模态基线方法显著提升了重建的精度、完整性和鲁棒性，这为利用先进机载与星载光学-SAR传感实现可扩展的高保真城市重建指明了可行路径。