Sharp feature extraction in point clouds

Sharp feature extraction has been playing an important role in point cloud processing. In this study, a novel method for extracting sharp features from point clouds is presented. It is proposed that in a given point cloud, the displacement between each of the points and the weighted average position in the given neighbourhood of that point is calculated, and the point is labelled as the candidate sharp feature point if the displacement is salient. The normal directions of the obtained candidate sharp feature points are estimated by means of local principal component analysis. Tensor voting is performed to refine the normal estimates. The displacement between a point and its locally weighted average position is projected along the estimated normal direction. The points with extreme projection values are defined as the final sharp feature points. The implementation of the proposed method on both synthesised and practical scanned point clouds show that the method is effective and robust for the purpose of sharp feature extraction.