# Reconstructing Building Height from Spaceborne TomoSAR Point Clouds Using a Dual-Topology Network

arXiv: https://arxiv.org/abs/2601.00658 · HTML: https://mlsanity.com/paper/2601.00658/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 47 references that are in the map.
- First posted on arXiv 2 Jan 2026.

## Builds on (most cited first, 17 of 17)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Convolutional Occupancy Networks](https://mlsanity.com/paper/2003.04618/) (2020)
- [IM2HEIGHT: Height Estimation from Single Monocular Imagery via Fully Residual Convolutional-Deconvolutional Network](https://mlsanity.com/paper/1802.10249/) (2018)
- [ALTO: Alternating Latent Topologies for Implicit 3D Reconstruction](https://mlsanity.com/paper/2212.04096/) (2022)
- [Large-scale Building Height Retrieval from Single SAR Imagery based on Bounding Box Regression Networks](https://mlsanity.com/paper/2111.09460/) (2021)
- [PolyGNN: Polyhedron-based Graph Neural Network for 3D Building Reconstruction from Point Clouds](https://mlsanity.com/paper/2307.08636/) (2023)
- [ImpliCity: City Modeling from Satellite Images with Deep Implicit Occupancy Fields](https://mlsanity.com/paper/2201.09968/) (2022)
- [HTC-DC Net: Monocular Height Estimation from Single Remote Sensing Images](https://mlsanity.com/paper/2309.16486/) (2023)
- [Deep-Learning-Based Single-Image Height Reconstruction from Very-High-Resolution SAR Intensity Data](https://mlsanity.com/paper/2111.02061/) (2021)
- [boldsymbolγ-Net: Superresolving SAR Tomographic Inversion via Deep Learning](https://mlsanity.com/paper/2112.04211/) (2021)
- [SAR Tomography at the Limit: Building Height Reconstruction Using Only 3-5 TanDEM-X Bistatic Interferograms](https://mlsanity.com/paper/2003.07803/) (2020)
- [Disentangled Latent Transformer for Interpretable Monocular Height Estimation](https://mlsanity.com/paper/2201.06357/) (2022)
- [Non-Local Compressive Sensing Based SAR Tomography](https://mlsanity.com/paper/1811.02046/) (2018)
- [Basis Pursuit Denoising via Recurrent Neural Network Applied to Super-resolving SAR Tomography](https://mlsanity.com/paper/2305.14209/) (2023)
- [HyperLISTA-ABT: An Ultra-light Unfolded Network for Accurate Multi-component Differential Tomographic SAR Inversion](https://mlsanity.com/paper/2309.16468/) (2023)

## Built on by (most cited first, 2 of 2)

- [The First Assessment of PhiSat-2 Imagery for Monocular Building Height Estimation](https://mlsanity.com/paper/2603.29245/) (2026)
- [Test-Time Adaptation for Height Completion via Self-Supervised ViT Features and Monocular Foundation Models](https://mlsanity.com/paper/2604.02009/) (2026)

## Abstract (arXiv)

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.