Reconstructing regular meshes from points: A parameterization-based approach

We propose an algorithm for reconstructing regular meshes from unorganized point clouds. At first, a nearly isometric point parameterization is computed using only the location of the points. A mesh, composed of nearly equilateral triangles, is later created using a regular sampling pattern. This approach produces meshes with high visual quality and suitable for use with applications such as finite element analysis, which tend to impose strong constraints on the regularity of the input mesh. Geometric properties, such as local connectivity and surface features, are identified directly from the points and are stored independent of the resulting mesh. This decoupling preserves most details and allows more flexibility for meshing. The resulting parameterization supports several direct applications, such as texturing and bump mapping. In addition, novel boundary identification and cut parameterization algorithms are proposed to overcome the difficulties caused by cuts, non-closed surfaces and possible self-overlapping parameter patches. We demonstrate the effectiveness of our approach by reconstructing regular meshes from real datasets, such as a human colon obtained from CT scan and objects digitized using laser scanners.