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DeepMesh: Differentiable Iso-Surface Extraction
[article]
2022
arXiv
pre-print
Geometric Deep Learning has recently made striking progress with the advent of continuous deep implicit fields. They allow for detailed modeling of watertight surfaces of arbitrary topology while not relying on a 3D Euclidean grid, resulting in a learnable parameterization that is unlimited in resolution. Unfortunately, these methods are often unsuitable for applications that require an explicit mesh-based surface representation because converting an implicit field to such a representation
arXiv:2106.11795v2
fatcat:lwuetyfe5nfo3fqtrwcy3qll5a