Cloud data modelling employing a unified, non-redundant triangular mesh

Abstract

This paper describes an application of error-based triangulation to very large sets of three-dimensional (3D) data. The algorithm is suitable for processing data collected by machine vision systems, co-ordinate measuring machines or laser-based range sensors. The algorithm models the large data sets, termed cloud data, using a unified, non-redundant triangular mesh. This is accomplished from the 3D data points in two steps. Firstly, an initial data thinning is performed, to reduce the copious data set size, employing 3D spatial filtering. Secondly, the triangulation commences utilizing a set of heuristic rules, from a user defined seed point. The triangulation algorithm interrogates the local geometric and topological information inherent in the cloud data points. The spatial filtering parameters are extracted from the cloud data set, by a series of local surface patches, and the required spatial error between the final triangulation and the cloud data. Two procedures are subsequently employed to enhance the mesh: (i) the edges of mesh triangles are adjusted to produce a mesh containing approximately equilateral triangles; and (ii) mesh edges are aligned with the boundaries present on the object to minimize smoothing of naturally occurring features. Case studies are presented that illustrate the efficacy of the technique for rapidly constructing a geometric model from 3D digitized data.