This paper demonstrates the definition of subdivision processes in nonlinear geometries such that smoothness of limits can be proved. We deal with curve subdivision in the presence of obstacles, in surfaces, in Riemannian manifolds, and in the Euclidean motion group. We show how to model kinematic surfaces and motions in the presence of obstacles via subdivision. As to numerics, we consider the sensitivity of the limit’s smoothness to sloppy computing.

A framework for determining the shortest path and the distance between every pair of vertices on a spatial network is presented. The framework, termed SILC, uses path coherence between the shortest path and the spatial positions of vertices on the spatial network, thereby, resulting in an encoding that is compact in representation and fast in path and distance retrievals. Using this framework, a wide variety of spatial queries such as incremental nearest neighbor searches and spatial distance joins can be shown to work on datasets of locations residing on a spatial network of sufficiently large size. The suggested framework is suitable for both main memory and disk-resident datasets. Categories and Subject Descriptors

Figure 1: A clay elephant statue (left) was modeled using sketch-based implicit-surface modeling software. Then, a lapped base texture and 25 feature textures were extracted from 22 images taken with a digital camera and composited on the surface. Photography, image creation, and texture positioning was completed in under an hour. A method is described for texturing surfaces using decals, images placed on the surface using local parameterizations. Decal parameterizations are generated with a novel O(N logN) discrete approximation to the exponential map which requires only a single additional step in Dijkstra’s graph-distance algorithm. Decals are dynamically composited in an interface that addresses many limitations of previous work. Tools for image processing, deformation/feature-matching, and vector graphics are implemented using direct surface interaction. Exponential map decals can contain holes and can also be combined with conformal parameterization to reduce distortion. The exponential map approximation can be computed on any point set, including meshes and sampled implicit surfaces, and is relatively stable under resampling. The decals stick to the surface as it is interactively deformed, allowing the texture to be preserved even if the surface changes topology. These properties make exponential map decals a suitable approach for texturing animated implicit surfaces.

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Abstract. Let S be the boundary of a convex polytope of dimension d + 1, or more generally let S be a convex polyhedral pseudomanifold. We prove that S has a polyhedral nonoverlapping unfolding into R d, so the metric space S is obtained from a closed (usually nonconvex) polyhedral ball in R d by identifying pairs of boundary faces isometrically. Our existence proof exploits geodesic flow away from a source point v ∈ S, which is the exponential map to S from the tangent space at v. We characterize the cut locus (the closure of the set of points in S with more than one shortest path to v) as a polyhedral complex in terms of Voronoi diagrams on facets. Analyzing infinitesimal expansion of the wavefront consisting of points at constant distance from v on S produces an algorithmic method for constructing Voronoi diagrams in each facet, and hence the unfolding of S. The algorithm, for which we provide pseudocode, solves the discrete geodesic problem. Its main construction generalizes the source unfolding for boundaries of 3-polytopes into R 2. We present conjectures concerning the number of shortest paths on the boundaries of convex polyhedra, and concerning continuous unfolding of convex polyhedra. We also comment on the intrinsic non-polynomial complexity of nonconvex manifolds.

3D models are now widely available for use in various applications. The demand for automatic model analysis and understanding is ever increasing. Model segmentation is an important step towards model understanding, and acts as a useful tool for different model processing applications, e.g. reverse engineering and modeling by example. We extend a random walk method used previously for image segmentation to give algorithms for both interactive and automatic model segmentation. This method is extremely efficient, and scales almost linearly with the number of faces, and the number of regions. For models of moderate size, interactive performance is achieved with commodity PCs. We demonstrate that this method can be applied to both triangle meshes and point cloud data. It is easy-to-implement, robust to noise in the model, and yields results suitable for downstream applications for both graphical and engineering models. Key words: model segmentation, random walks, interactive

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In geometric modeling and processing, computer graphics and computer vision, smooth surfaces are approximated by discrete triangular meshes reconstructed from sample points on the surfaces. A fundamental problem is to design rigorous algorithms to guarantee the geometric approximation accuracy by controlling the sampling density. This paper gives explicit formulae to the bounds of Hausdorff distance, normal distance and Riemannian metric distortion between the smooth surface and the discrete mesh in terms of principle curvature and the radii of geodesic circum-circle of the triangles. These formulae can be directly applied to design sampling density for data acquisitions and surface reconstructions. Furthermore, we prove that the meshes induced from the Delaunay triangulations of the dense samples on a smooth surface are convergent to the smooth surface under both Hausdorff distance and normal fields. The Riemannian metrics and the Laplace-Beltrami operators on the meshes are also convergent to those on the smooth surfaces. These theoretical results lay down the foundation for a broad class of reconstruction and approximation algorithms in geometric modeling and processing. Practical algorithms for approximating surface Delaunay triangulations are introduced based on global conformal surface parameterizations and planar Delaunay triangulations. Thorough experiments are conducted to support the theoretical results.

Sculptural reliefs are widely used in various industries for purposes such as applying brands to packaging and decorating porcelain. In order to easily apply reliefs to CAD models, it is often desirable to reverse-engineer previously designed and manufactured reliefs. 3D scanners can generate triangle meshes from objects with reliefs; however, previous mesh segmentation work has not considered the particular problem of separation of reliefs from background. We consider here the specific case of segmenting a simple relief delimited by a single outer contour, which lies on a smooth, slowly varying background. Generally, such reliefs meet the surrounding surface in a small step, enabling us to devise a specific method for such relief segmentation. We find the boundary between the background and the relief using an adaptive snake. It starts at a simple user-drawn contour, and is driven inwards by a collapsing force until it matches the relief's boundary. Our method is insensitive to the choice of the initial contour. The snake's limiting position is controlled by a feature energy term designed to find a step. A refinement strategy is then used to drive the snake into concavities of the relief contour. We demonstrate operation of our algorithm using real scanned models with different relief contour shapes and triangle meshes with different resolutions.

3D mesh models are now widely available for use in various applications. The demand for automatic model analysis and understanding is ever increasing. Mesh segmentation is an important step towards model understanding, and acts as a useful tool for different mesh processing applications, e.g. reverse engineering and modeling by example. We extend a random walk method used previously for image segmentation to give algorithms for both interactive and automatic mesh segmentation. This method is extremely efficient, and scales almost linearly with increasing number of faces. For models of moderate size, interactive performance is achieved with commodity PCs. It is easy-to-implement, robust to noise in the mesh, and yields results suitable for downstream applications for both graphical and engineering models.