A Voxel Graph-Based Resampling Approach for the Aerial Laser Scanning of Urban Buildings

This letter presents a resampling algorithm for the refinement of the aerial laser scanning of urban buildings. We aim at resolving the problems of noise, variable density, and vacancies in the 3-D laser scanning. The assumption is that the points are locally sampled from piecewise building surfaces, so we can generate a new point cloud to present a plausible approximation of the surface based on the local smooth trait. First, we introduce an oversegmentation approach to generate super voxels by analyzing local geometric similarities. Then, each voxel is treated as a node composing a graph. Based on the combination method of the graph-constrained filtering and the interpolation, the resampling approach is designed to reproject the points to their optimized positions. This approach could ensure that the resampling points are faithful to the initial data and maintain a global even density. Furthermore, we evaluated the effectiveness and efficiency of the proposed method on the applications of geometry primitive extraction and surface modeling. The experiments demonstrate that the approach is suitable for improving the quality of aerial scanning data in different aspects.