NeuralMeshing: Differentiable Meshing of Implicit Neural Representations
- URL: http://arxiv.org/abs/2210.02382v1
- Date: Wed, 5 Oct 2022 16:52:25 GMT
- Title: NeuralMeshing: Differentiable Meshing of Implicit Neural Representations
- Authors: Mathias Vetsch, Sandro Lombardi, Marc Pollefeys and Martin R. Oswald
- Abstract summary: We propose a novel differentiable meshing algorithm for extracting surface meshes from neural implicit representations.
Our method produces meshes with regular tessellation patterns and fewer triangle faces compared to existing methods.
- Score: 63.18340058854517
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: The generation of triangle meshes from point clouds, i.e. meshing, is a core
task in computer graphics and computer vision. Traditional techniques directly
construct a surface mesh using local decision heuristics, while some recent
methods based on neural implicit representations try to leverage data-driven
approaches for this meshing process. However, it is challenging to define a
learnable representation for triangle meshes of unknown topology and size and
for this reason, neural implicit representations rely on non-differentiable
post-processing in order to extract the final triangle mesh. In this work, we
propose a novel differentiable meshing algorithm for extracting surface meshes
from neural implicit representations. Our method produces the mesh in an
iterative fashion, which makes it applicable to shapes of various scales and
adaptive to the local curvature of the shape. Furthermore, our method produces
meshes with regular tessellation patterns and fewer triangle faces compared to
existing methods. Experiments demonstrate the comparable reconstruction
performance and favorable mesh properties over baselines.
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