  Building models are conventionally reconstructed by building roof points
planar segmentation and then using a topology graph to group the planes
together. Roof edges and vertices are then mathematically represented by
intersecting segmented planes. Technically, such solution is based on
sequential local fitting, i.e., the entire data of one building are not
simultaneously participating in determining the building model. As a
consequence, the solution is lack of topological integrity and geometric rigor.
Fundamentally different from this traditional approach, we propose a holistic
parametric reconstruction method which means taking into consideration the
entire point clouds of one building simultaneously. In our work, building
models are reconstructed from predefined parametric (roof) primitives. We first
use a well-designed deep neural network to segment and identify primitives in
the given building point clouds. A holistic optimization strategy is then
introduced to simultaneously determine the parameters of a segmented primitive.
In the last step, the optimal parameters are used to generate a watertight
building model in CityGML format. The airborne LiDAR dataset RoofN3D with
predefined roof types is used for our test. It is shown that PointNet++ applied
to the entire dataset can achieve an accuracy of 83% for primitive
classification. For a subset of 910 buildings in RoofN3D, the holistic approach
is then used to determine the parameters of primitives and reconstruct the
buildings. The achieved overall quality of reconstruction is 0.08 meters for
point-surface-distance or 0.7 times RMSE of the input LiDAR points. The study
demonstrates the efficiency and capability of the proposed approach and its
potential to handle large scale urban point clouds.
