ManifoldPlus: A Robust and Scalable Watertight Manifold Surface Generation Method for Triangle Soups

We present ManifoldPlus, a method for robust and scalable conversion of triangle soups to watertight manifolds. While many algorithms in computer graphics require the input mesh to be a watertight manifold, in practice many meshes designed by artists are often for visualization purposes, and thus have non-manifold structures such as incorrect connectivity, ambiguous face orientation, double surfaces, open boundaries, self-intersections, etc. Existing methods suffer from problems in the inputs with face orientation and zero-volume structures. Additionally most methods do not scale to meshes of high complexity. In this paper, we propose a method that extracts exterior faces between occupied voxels and empty voxels, and uses a projection-based optimization method to accurately recover a watertight manifold that resembles the reference mesh. Compared to previous methods, our methodology is simpler. It does not rely on face normals of the input triangle soups and can accurately recover zero-volume structures. Our algorithm is scalable, because it employs an adaptive Gauss-Seidel method for shape optimization, in which each step is an easy-to-solve convex problem. We test ManifoldPlus on ModelNet10 and AccuCity datasets to verify that our methods can generate watertight meshes ranging from object-level shapes to city-level models. Furthermore, through our experimental evaluations, we show that our method is more robust, efficient and accurate than the state-of-the-art. Our implementation is publicly available

Fast Tetrahedral Meshing in the Wild

We propose a new tetrahedral meshing method, fTetWild, to convert triangle soups into high-quality tetrahedral meshes. Our method builds on the TetWild algorithm, replacing the rational triangle insertion with a new incremental approach to construct and optimize the output mesh, interleaving triangle insertion and mesh optimization. Our approach makes it possible to maintain a valid floating-point tetrahedral mesh at all algorithmic stages, eliminating the need for costly constructions with rational numbers used by TetWild, while maintaining full robustness and similar output quality. This allows us to improve on TetWild in two ways. First, our algorithm is significantly faster, with running time comparable to less robust Delaunay-based tetrahedralization algorithms. Second, our algorithm is guaranteed to produce a valid tetrahedral mesh with floating-point vertex coordinates, while TetWild produces a valid mesh with rational coordinates which is not guaranteed to be valid after floating-point conversion. As a trade-off, our algorithm no longer guarantees that all input triangles are present in the output mesh, but in practice, as confirmed by our tests on the Thingi10k dataset, the algorithm always succeeds in inserting all input triangles