Reconstructing Building Height from Spaceborne TomoSAR Point Clouds Using a Dual-Topology Network
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866914230372925440 |
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| author | Chen, Zhaiyu Wang, Yuanyuan Shi, Yilei Zhu, Xiao Xiang |
| author_facet | Chen, Zhaiyu Wang, Yuanyuan Shi, Yilei Zhu, Xiao Xiang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_00658 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Reconstructing Building Height from Spaceborne TomoSAR Point Clouds Using a Dual-Topology Network Chen, Zhaiyu Wang, Yuanyuan Shi, Yilei Zhu, Xiao Xiang Computer Vision and Pattern Recognition 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. |
| title | Reconstructing Building Height from Spaceborne TomoSAR Point Clouds Using a Dual-Topology Network |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.00658 |