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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.

Triangular meshes are important in many fields in both basic and applied research that rely on their correctness and accuracy. Many operations with meshes can lead to undesirable situations and the resulting models can be damaged and further unusable. Self-intersection and mesh-to-mesh intersection are types of operations that are often present and can cause such problems. We propose an accurate geometry-based method for local repair of intersecting meshes. The state-of-the-art methods either solve the problem inaccurately, or use methods such as arbitrary precision arithmetic or virtual perturbation to deal with the troublesome boundary cases. Our method represents a robust way to repair intersecting meshes accurately without the need to manipulate with the input data or to employ arbitrary precision arithmetic. The correct solution is obtained through a careful classification of the cases that could result from a numerical imprecision of the floating point arithmetic.