Feature enhancing aerial lidar point cloud refinement

Raw aerial LiDAR point clouds often suffer from noise and under-sampling, which can be alleviated by feature preserving refinement. However, existing approaches are limited to only preserving normal discontinuous features (ridges, ravines and crest lines) while position discontinuous features (boundaries) are also universal in urban scenes. We present a new refinement approach to accommodate unique properties of aerial LiDAR building points. By extending recent developments in geometry refinement to explicitly regularize boundary points, both normal and position discontinuous features are preserved and enhanced. The refinement includes two steps: i) the smoothing step applies a two-stage feature preserving bilateral filtering, which first filters normals and then updates positions under the guidance of the filtered normals. In a separate similar process, boundary points are smoothed directed by tangent directions of underlying lines, and ii) the up-sampling step interpolates new points to fill gaps/holes for both interior surfaces and boundary lines, through a local gap detector and a feature-aware bilateral projector. Features can be further enhanced by limiting the up-sampling near discontinuities. The refinement operates directly on points with diverse density, shape and complexity. It is memory-efficient, easy to implement, and easily extensible. © (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.