We present PolyGNN, a polyhedron-based graph neural network for 3D building reconstruction from point clouds. PolyGNN learns to assemble primitives obtained by polyhedral decomposition via graph node classification, achieving a watertight and compact reconstruction. To effectively represent arbitrary-shaped polyhedra in the neural network, we propose a skeleton-based sampling strategy to generate polyhedron-wise queries. These queries are then incorporated with inter-polyhedron adjacency to enhance the classification. PolyGNN is end-to-end optimizable and is designed to accommodate variable-size input points, polyhedra, and queries with an index-driven batching technique. To address the abstraction gap between existing city-building models and the underlying instances, and provide a fair evaluation of the proposed method, we develop our method on a large-scale synthetic dataset with well-defined ground truths of polyhedral labels. We further conduct a transferability analysis across cities and on real-world point clouds. Both qualitative and quantitative results demonstrate the effectiveness of our method, particularly its efficiency for large-scale reconstructions.@en

In this paper, we propose a framework referred to as 'geodetic synthetic aperture radar (SAR) tomography' that fuses the SAR imaging geodesy and tomographic SAR inversion (TomoSAR) approaches to obtain absolute 3-D positions of a large amount of natural scatterers. The methodology is applied on four very high resolution TerraSAR-X spotlight image stacks acquired over the city of Berlin. Since all the TomoSAR estimates are relative to the same reference point object whose absolute 3-D positions are retrieved by means of stereo SAR, the point clouds reconstructed using data acquired from different viewing angles can be geodetically fused. To assess the accuracy of the position estimates, the resulting absolute shadow-free 3-D TomoSAR point clouds are compared with a digital surface model obtained by airborne LiDAR. It is demonstrated that an absolute positioning accuracy of around 20 cm and a meter-order relative positioning accuracy can be achieved by the proposed framework using TerraSAR-X data.

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Synthetic Aperture Radar Tomography (TomoSAR) coupled with data from modern SAR sensors, such as the German TerraSAR-X (TS-X) produces the most detailed three-dimensional (3D) maps by distinguishing among multiple scatterers within a resolution cell. Furthermore, multi-temporal TomoSAR allows for recording the underlying deformation phenomenon of each individual scatterer. One of the limitations of using InSAR techniques, including TomoSAR, is that they only measure deformation along the radar Line-of-Sight (LOS). In order to enhance the understanding of deformation, a decomposition of the observed LOS displacement into the 3D deformation vector in the local coordinate system is desired. In this paper we propose a method, based on L1 norm minimization within local spatial cubes, to reconstruct 3D deformation vectors from TomoSAR point clouds available from, at least, three different viewing geometries. The methodology is applied on two pair of cross-heading TS-X spotlight image stacks over the city of Berlin. The linear deformation rate and amplitude of seasonal deformation are decomposed and the results from two individual test sites with remarkable deformation patterns are discussed in details.@en

In this paper, we propose a framework referred to as 'geodetic TomoSAR' that fuses the SAR image geodesy and TomoSAR approaches to obtain absolute 3D positions of a large amount of natural scatterers. The methodology is applied on four Very High Resolution (VHR) TerraSAR-X spotlight image stacks acquired over the city of Berlin. Since the TomoSAR estimates are referred to the identical reference point whose absolute 3D positions are retrieved by means of Stereo-SAR, the point clouds from ascending and descending orbits are automatically fused. To assess the accuracy of the position estimates, the resulting absolute shadow-free 3D TomoSAR point clouds are compared to a DSM obtained by airborne LiDAR.

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