We use the p -Laplacian with large p -values in order to approximate geodesic distances to features on surfaces. This differs from Fayolle and Belyaev's ( 2018 ) computational results using the p -Laplacian for the distance-to-surface problem. Our approach appears to offer some distinct advantages over other popular PDE-based distance function approximation methods. We employ a surface finite element scheme and demonstrate numerical convergence to the true geodesic distance functions. We check that our numerical results adhere to the triangle inequality and examine robustness against geometric noise such as vertex perturbations. We also present comparisons of our method with the heat method from Crane et al. (2017) and the classical polyhedral method from Mitchell et al. (1987) .

Many geometry processing techniques require the solution of partial differential equations (PDEs) on manifolds embedded in ℝ 2 or ℝ 3 , such as curves or surfaces. Such manifold PDEs often involve boundary conditions (e.g., Dirichlet or Neumann) prescribed at points or curves on the manifold’s interior or along the geometric (exterior) boundary of an open manifold. However, input manifolds can take many forms (e.g., triangle meshes, parametrizations, point clouds, implicit functions, etc.). Typically, one must generate a mesh to apply finite element-type techniques or derive specialized discretization procedures for each distinct manifold representation. We propose instead to address such problems in a unified manner through a novel extension of the closest point method (CPM) to handle interior boundary conditions. CPM solves the manifold PDE by solving a volumetric PDE defined over the Cartesian embedding space containing the manifold and requires only a closest point representation of the manifold. Hence, CPM supports objects that are open or closed, orientable or not, and of any codimension. To enable support for interior boundary conditions, we derive a method that implicitly partitions the embedding space across interior boundaries. CPM’s finite difference and interpolation stencils are adapted to respect this partition while preserving second-order accuracy. Additionally, we develop an efficient sparse-grid implementation and numerical solver that can scale to tens of millions of degrees of freedom, allowing PDEs to be solved on more complex manifolds. We demonstrate our method’s convergence behavior on selected model PDEs and explore several geometry processing problems: diffusion curves on surfaces, geodesic distance, tangent vector field design, harmonic map construction, and reaction-diffusion textures. Our proposed approach thus offers a powerful and flexible new tool for a range of geometry processing tasks on general manifold representations.

The need to compare separate objects arises in a wide range of applications. In one approach for comparing objects, `Shape-DNA' is constructed to give a numerical fingerprint representing an individual object. Shape-DNA is a cropped set of eigenvalues of the Laplace-Beltrami operator for the surface of the object. In this paper, we compute the Shape-DNA of surfaces using the closest point method. Our approach may be applied to a variety of surface representations including triangulations, point clouds and certain analytical shapes. A 2D multidimensional scaling plot illustrates that similar objects form groups based on the Shape-DNAs. Our method has the benefit that it may be applied to surfaces defined by dense point clouds without requiring the construction of point connectivity.

We propose a method to detect objects and patterns in textures on general surfaces. Our approach applies the Chan-Vese variational model for active contours without edges to the problem of segmentation of scalar surface data. This leads to gradient descent equations which are level set equations on surfaces. These equations are evolved using the Closest Point Method, which is a recent technique for solving partial differential equations (PDEs) on surfaces. The final algorithm has a particularly simple form: it merely alternates a time step of the usual Chan-Vese model in a small 3D neighborhood of the surface with an interpolation step. We remark that the method can treat very general surfaces since it uses a closest point function to represent the underlying surface. Various experimental results are presented, including segmentation on smooth surfaces, non-smooth surfaces, open surfaces, and general triangulated surfaces.

We propose a very efficient numerical algorithm for minimizing certain curvature dependent functionals that appear in a variety of well known variational models of image processing and computer vision. It has many applications, such as image inpainting, image segmentation, and surface fairing in computer graphics. The proposed algorithm is generalized to continuous interface version that has better resolution while keeping the same formal complexity. As an example, we apply our technique to a shape reconstruction problem based on Euler's elastica.