Sharp Feature Detection on Point Sets via Dictionary Learning and Sparse Coding

摘要

In this paper, a new approach for detecting sharp features on point set is presented. Detecting sharp features is an essential stage for structuring point sets as a previous step for feature lines reconstruction, surface estimation, and non-photorealistic rendering. Detecting sharp features in a point sets is not an easy task, because, without topological information that connects the points between them, only the intrinsic information bringing by the raw points as discrete geometric properties is available to carry out the feature detection. The proposed algorithm uses the eigenvectors and eigenvalues of the covariance matrix from a given neighborhood in the point set, to estimate the surface variation, the sphericity and the orthogonal distance of each point to the regression plane to its neighborhood, to construct a feature vector to every point in the point set. Next, we use this feature vectors as basis signals to carry out a dictionary learning process to get a trained dictionary; then we perform the corresponding sparse coding process to get the sparse matrix. Finally analyzing the sparse matrix, it is determined which feature vectors correspond to points that are candidates to be selected as sharp features. The robustness of our method is demonstrated on 3D objects with and without added noisy.