To definite and compute differential invariants, like curvatures, for triangular meshes (or polyhedral surfaces) is a key problem in CAGD and the computer vision. The Gaussian curvature and the mean curvature are determined by the differential of the Gauss map of the underlying surface. The Gauss map assigns to each point in the surface the unit normal vector of the tangent plane to the surface at this point. We follow the ideas developed in Chen and Wu Chen2 (2004) and Wu, Chen and Chi Wu (2005) to describe a new and simple approach to estimate the differential of the Gauss map and curvatures from the viewpoint of the gradient and the centroid weights. This will give us a much better estimation of curvatures than Taubin's algorithm Taubin (1995).
Flynn and Jain @cite_9 (1989) used a suitable sphere passing through four vertices to estimate curvatures. Meek and Walton @cite_2 (2000) examined several methods and compared them with the discretization and interpolation method. Gatzke and Grim @cite_3 (2003) systematically analyzed the results of computation of curvatures of surfaces represented by triangular meshes and recommended the surface fitting methods. See also Petitjean @cite_7 (2002) for the surface fitting methods. @cite_5 (2003) employed the Gauss-Bonnet theorem to estimate the Gaussian curvatures and introduced the Laplace-Beltrami operator to approximate the mean curvature.
@cite_7 @cite_9 @cite_3 @cite_2 @cite_5
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This paper takes a systematic look at methods for estimating the curvature of surfaces represented by triangular meshes. We have developed a suite of test cases for assessing both the detailed behavior of these methods, and the error statistics that occur for samples from a general mesh. Detailed behavior is represented by the sensitivity of curvature calculation methods to noise, mesh resolution, and mesh regularity factors. Statistical analysis breaks out the effects of valence, triangle shape, and curvature sign. These tests are applied to existing discrete curvature approximation techniques and common surface fitting methods. We provide a summary of existing curvature estimation methods, and also look at alternatives to the standard parameterization techniques. The results illustrate the impact of noise and mesh related issues on the accuracy of these methods and provide guidance in choosing an appropriate method for applications requiring curvature estimates. This paper takes a systematic look at calculating the curvature of surfaces represented by triangular meshes. We have developed a suite of test cases for assessing the sensitivity of curvature calculations, to noise, mesh resolution, and mesh regularity. These tests are applied to existing discrete curvature approximation techniques and three common surface fitting methods (polynomials, radial basis functions and conics). We also introduce a modification to the standard parameterization technique. Finally, we examine the behaviour of the curvature calculation techniques in the context of segmentation. While it is usually not difficult to compute principal curvatures of a smooth surface of sufficient differentiability, it is a rather difficult task when only a polygonal approximation of the surface is available, because of the inherent ambiguity of such representation. A number of different approaches has been proposed in the past that tackle this problem using various techniques. Most papers tend to focus on a particular method, while an comprehensive comparison of the different approaches is usually missing. We present results of a large experiment, involving both common and recently proposed curvature estimation techniques, applied to triangle meshes of varying properties. It turns out that none of the approaches provides reliable results under all circumstances. Motivated by this observation, we investigate mesh statistics, which can be computed from vertex positions and mesh connectivity information only, and which can help in deciding which estimator will work best for a particular case. Finally, we propose a meta-estimator, which makes a choice between existing algorithms based on the value of the mesh statistics, and we demonstrate that such meta-estimator, despite its simplicity, provides considerably more robust results than any existing approach. In this paper, we develop methods to rapidly remove rough features from irregularly triangulated data intended to portray a smooth surface. The main task is to remove undesirable noise and uneven edges while retaining desirable geometric features. The problem arises mainly when creating high-fidelity computer graphics objects using imperfectly-measured data from the real world. Our approach contains three novel features: an implicit integration method to achieve efficiency, stability, and large time-steps; a scale-dependent Laplacian operator to improve the diffusion process; and finally, a robust curvature flow operator that achieves a smoothing of the shape itself, distinct from any parameterization. Additional features of the algorithm include automatic exact volume preservation, and hard and soft constraints on the positions of the points in the mesh. We compare our method to previous operators and related algorithms, and prove that our curvature and Laplacian operators have several mathematically-desirable qualities that improve the appearance of the resulting surface. In consequence, the user can easily select the appropriate operator according to the desired type of fairing. Finally, we provide a series of examples to graphically and numerically demonstrate the quality of our results.