Constructing a (signed) distance field and contouring its zero level set are two important steps in many surface reconstruction methods. While most high-quality distance transforms compute the distance to the surface as well as the closest point on it, the contouring step typically uses only the distance and omits the closest point information. Our novel closest point contouring algorithm (CPC) uses the full closest point field, and, thus, allows improving existing methods for high-quality triangle mesh reconstruction based on implicit function models: Since we select the vertex positions directly from the set of closest points, all triangle vertices are guaranteed to lie exactly on the zero-contour and no approximations are necessary. By employing recent findings in the context of so-called embedding techniques, we derive a formulation of the mean curvature vector on the closest point representation and use this formulation to properly select the vertices to be triangulated. In combination with a new table-based triangulation scheme this allows us to detect and preserve sharp features, and to avoid small degenerated triangles in smooth areas. CPC can handle open and non-orientable surfaces, and its data-parallel nature makes it well suited for GPUs.

We present an Eulerian method for the real‐time simulation of intrinsic fluid dynamics effects on deforming surfaces. Our method is based on a novel semi‐Lagrangian closest point method for the solution of partial differential equations on animated triangle meshes. We describe this method and demonstrate its use to compute and visualize flow and wave propagation along such meshes at high resolution and speed. Underlying our technique is the efficient conversion of an animated triangle mesh into a time‐dependent implicit representation based on closest surface points. The proposed technique is unconditionally stable with respect to the surface deformation and, in contrast to comparable Lagrangian techniques, its precision does not depend on the level of detail of the surface triangulation.

The authors propose a framework to encrypt Baseline JPEG files directly at bitstream level, i.e., without the need to recompress them. The authors’ approach enables encrypting more than 25 pictures per second in VGA resolution, allowing real-time operation in typical video surveillance applications. In addition, their approach preserves the length of the bitstream while being completely format-compliant. Furthermore, the authors show that an attack on the encryption process, which partly relies on AES, is practically infeasible.

The Closest Point Method (CPM) is a method for numerically solving partial differential equations (PDEs) on arbitrary surfaces, independent of the existence of a surface parametrization. The CPM uses a closest point representation of the surface, to solve the unmodified Cartesian version of a surface PDE in a 3D volume embedding, using simple and well‐understood techniques. In this paper, we present the numerical solution of the wave equation and the incompressible Navier‐Stokes equations on surfaces via the CPM, and we demonstrate surface appearance and shape variations in real‐time using this method. To fully exploit the potential of the CPM, we present a novel GPU realization of the entire CPM pipeline. We propose a surface‐embedding adaptive 3D spatial grid for efficient representation of the surface, and present a high‐performance approach using CUDA for converting surfaces given by triangulations into this representation. For real‐time performance, CUDA is also used for the numerical procedures of the CPM. For rendering the surface (and the PDE solution) directly from the closest point representation without the need to reconstruct a triangulated surface, we present a GPU ray‐casting method that works on the adaptive 3D grid.