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Learning and Meshing from Deep Implicit Surface Networks Using an Efficient Implementation of Analytic Marching
[article]
2021
arXiv
pre-print
Reconstruction of object or scene surfaces has tremendous applications in computer vision, computer graphics, and robotics. In this paper, we study a fundamental problem in this context about recovering a surface mesh from an implicit field function whose zero-level set captures the underlying surface. To achieve the goal, existing methods rely on traditional meshing algorithms; while promising, they suffer from loss of precision learned in the implicit surface networks, due to the use of
arXiv:2106.10031v1
fatcat:7btqis7nkjcfzkbytg6725ipjm