Point set silhouettes via local reconstruction

We present an algorithm to compute the silhouette set of a point cloud. Previous methods extract point set silhouettes by thresholding point normals, which can lead to simultaneous over- and under-detection of silhouettes. We argue that additional information such as surface curvature is necessary to resolve these issues. To this end, we develop a local reconstruction scheme using Gabriel and intrinsic Delaunay criteria and define point set silhouettes based on the notion of a silhouette-generating set. The mesh umbrellas, or local reconstructions of one-ring triangles surrounding each point sample, generated by our method enable accurate silhouette identification near sharp features and close-by surface sheets, and provide the information necessary to detect other characteristic curves such as creases and boundaries. We show that these curves collectively provide a sparse and intuitive visualisation of point-cloud data.

Graphical abstractFigure optionsDownload full-size imageDownload high-quality image (196 K)Download as PowerPoint slideHighlights► We extend the definition of mesh silhouettes to include point clouds. ► From this definition, we develop a local reconstruction scheme for point clouds. ► Our reconstruction captures sharp features and boundaries from a small neighbourhood. ► This enables accurate silhouette extraction and feature enhancement.