  When obtaining interior 3D voxel data from triangular meshes, most existing
methods fail to handle low quality meshes which happens to take up a big
portion on the internet. In this work we present a robust voxelization method
that is based on tetrahedral mesh generation within a user defined error bound.
Comparing to other tetrahedral mesh generation methods, our method produces
much higher quality tetrahedral meshes as the intermediate outcome, which
allows us to utilize a faster voxelization algorithm that is based on a
stronger assumption. We show the results comparing to various methods including
the state-of-the-art. Our contribution includes a framework which takes
triangular mesh as an input and produces voxelized data, a proof to an unproved
algorithm that performs better than the state-of-the-art, and various
experiments including parallelization built on the GPU and CPU. We further
tested our method on various dataset including Princeton ModelNet and Thingi10k
to show the robustness of the framework, where near 100% availability is
achieved, while others can only achieve around 50%.
