The alpha complex, a subset of the Delaunay triangulation, has been extensively used as the underlying representation for biomolecular structures. We propose a GPU-based parallel algorithm for the computation of the alpha complex, which exploits the knowledge of typical spatial distribution and sizes of atoms in a biomolecule. Unlike existing methods, this algorithm does not require prior construction of the Delaunay triangulation. The algorithm computes the alpha complex in two stages. The first stage proceeds in a bottom-up fashion and computes a superset of the edges, triangles, and tetrahedra belonging to the alpha complex. The false positives from this estimation stage are removed in a subsequent pruning stage to obtain the correct alpha complex. Computational experiments on several biomolecules demonstrate the superior performance of the algorithm, up to a factor of 50 when compared to existing methods that are optimized for biomolecules.

This paper presents an algorithm for sampling and triangulating a generic $C^2$-smooth surface $\Sigma\subset \mathbb{R}^3$ that is input with an implicit equation. The output triangulation is guaranteed to be homeomorphic to $\Sigma$. We also prove that the triangulation has well-shaped triangles, large dihedral angles, and a small size. The only assumption we make is that the input surface representation is amenable to certain types of computations, namely, computations of the intersection points of a line and $\Sigma$, computations of the critical points in a given direction, and computations of certain silhouette points.

This paper presents an algorithm for sampling and triangulatinga smooth surface Σ ⊂ ℝ3 where the triangulation is homeomorphic to Σ. The only assumption we make is that the input surface representation is amenable to certain types of computations, namely computations of the intersection points of a line with the surface, computations of the critical points of some height functions defined on the surface and its restriction to a plane, and computations of some silhouette points. The algorithm ensures bounded aspect ratio, size optimality, and smoothness of the output triangulation. Unlike previous algorithms, this algorithm does not need to compute the local feature size for generating the sample points which was a major bottleneck. Experiments show the usefulness of the algorithm in remeshing and meshing CAD surfaces that are piecewise smooth.

We present an algorithm to compute a Delaunay mesh conforming to a polyhedron possibly with small input angles. The radius-edge ratio ofmost output tetrahedra are bounded by a constant, except possibly those that are provably close to small angles. Further, the mesh is graded, that is, edge lengths are at least a constant fraction of the local feature sizes at the edge endpoints. Unlike a previous algorithm, this algorithm is simple to implement as it avoids computing local feature sizes and protective zones explicitly. Our experimental results confirm our claims and show that few skinny tetrahedra remain.