Combining an angle criterion with voxelization and the flying voxel method in reconstructing building models from LiDAR data

Traditional documentation capabilities of laser scanning technology can be further exploited for urban modeling through the transformation of resulting point clouds into solid models compatible for computational analysis. This article introduces such a technique through the combination of an angle criterion and voxelization. As part of that, a k-nearest neighbor (kNN) searching algorithm is implemented using a predefined number of kNN points combined with a maximum radius of the neighborhood, something not previously implemented. From this sample, points are categorized as boundary or interior points based on an angle criterion. Façade features are determined based on underlying vertical and horizontal grid voxels of the feature boundaries by a grid clustering technique. The complete building model involving all full voxels is generated by employing the Flying Voxel method to relabel voxels that are inside openings or outside the façade as empty voxels. Experimental results on three different buildings, using four distinct sampling densities showed successful detection of all openings, reconstruction of all building façades, and automatic filling of all improper holes. The maximum nodal displacement divergence was 1.6% compared to manually generated meshes from measured drawings. This fully automated approach rivals processing times of other techniques with the distinct advantage of extracting more boundary points, especially in less dense data sets (<175 points/m2), which may enable its more rapid exploitation of aerial laser scanning data and ultimately preclude needing a priori knowledge.