Point2Building: Reconstructing Buildings from Airborne LiDAR Point
  Clouds
Yujia Liu Anton Obukhov Jan Dirk Wegner Konrad Schindler
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.