Point2Building: Reconstructing Buildings from Airborne LiDAR Point Clouds
This addresses the problem of accurate 3D building reconstruction for urban planning and mapping, though it is incremental as it builds on existing learning-based approaches.
The paper tackles the problem of reconstructing 3D polygonal meshes of buildings from airborne LiDAR point clouds, which is challenging due to diverse designs, low point density, and incomplete coverage. The result is a learning-based method that shows good generalization across diverse urban styles, as validated on data from Zurich, Berlin, and Tallinn.
We present a learning-based approach to reconstruct buildings as 3D polygonal meshes from airborne LiDAR point clouds. What makes 3D building reconstruction from airborne LiDAR hard is the large diversity of building designs and especially roof shapes, the low and varying point density across the scene, and the often incomplete coverage of building facades due to occlusions by vegetation or to the viewing angle of the sensor. To cope with the diversity of shapes and inhomogeneous and incomplete object coverage, we introduce a generative model that directly predicts 3D polygonal meshes from input point clouds. Our autoregressive model, called Point2Building, iteratively builds up the mesh by generating sequences of vertices and faces. This approach enables our model to adapt flexibly to diverse geometries and building structures. Unlike many existing methods that rely heavily on pre-processing steps like exhaustive plane detection, our model learns directly from the point cloud data, thereby reducing error propagation and increasing the fidelity of the reconstruction. We experimentally validate our method on a collection of airborne LiDAR data of Zurich, Berlin and Tallinn. Our method shows good generalization to diverse urban styles.