Reliable building height estimation is essential for various urban applications. Spaceborne SAR tomography (TomoSAR) provides weather-independent, side-looking observations that capture facade-level structure, offering a promising alternative to conventional optical methods. However, TomoSAR point clouds often suffer from noise, anisotropic point distributions, and data voids on incoherent surfaces, all of which hinder accurate height reconstruction. To address these challenges, we introduce a learning-based framework for converting raw TomoSAR points into high-resolution building height maps. Our dual-topology network alternates between a point branch that models irregular scatterer features and a grid branch that enforces spatial consistency. By jointly processing these representations, the network denoises the input points and inpaints missing regions to produce continuous height estimates. To our knowledge, this is the first proof of concept for large-scale urban height mapping directly from TomoSAR point clouds. Extensive experiments on data from Munich and Berlin validate the effectiveness of our approach. Moreover, we demonstrate that our framework can be extended to incorporate optical satellite imagery, further enhancing reconstruction quality. The source code is available at https://github.com/zhu-xlab/tomosar2height.

翻译：可靠的建筑高度估计对于各类城市应用至关重要。星载合成孔径雷达层析成像（TomoSAR）提供不受天气影响的侧视观测，能够捕获建筑立面级别的结构，为传统光学方法提供了一种有前景的替代方案。然而，TomoSAR点云通常存在噪声、各向异性的点分布以及在非相干表面的数据空洞等问题，这些都阻碍了准确的高度重建。为应对这些挑战，我们提出了一种基于学习的框架，用于将原始TomoSAR点转换为高分辨率建筑高度图。我们的双拓扑网络交替处理一个用于建模不规则散射体特征的点分支，以及一个用于加强空间一致性的网格分支。通过联合处理这两种表征，该网络对输入点进行去噪并修复缺失区域，从而生成连续的高度估计。据我们所知，这是首个直接从TomoSAR点云进行大规模城市高度制图的概念验证。在慕尼黑和柏林数据上进行的大量实验验证了我们方法的有效性。此外，我们证明了该框架可以扩展以融合光学卫星影像，从而进一步提升重建质量。源代码可在 https://github.com/zhu-xlab/tomosar2height 获取。