PoU based sharp features extraction from point cloud

Sharp features of 3D point clouds play an important role in many geometric computations and modeling application. In this paper, a novel modified Partition of Unity (PoU) Based Sharp feature extraction algorithm is proposed, which is directly operated on discrete point clouds. For every point in target point cloud, spherical neighborhood with radius δ is acquired with the help of KD-Tree and weighted average position of points within the δ-neighborhood is computed using modified PoU method. Distance which is the projection of the displace between original point and its Weighted average position along normal direction is defined as the criteria for a point belong to sharp feature or crease line. Experiments on both synthetic data and practical scanner point clouds indicate that our algorithm are both efficient and effective to the task of sharp feature extraction from point clouds. Our method is easy to be implemented and more sensitive to sharp features as well as its low computational complexity.