NeuralMeshing: Differentiable Meshing of Implicit Neural Representations
Overview
This is a work which we submitted to GCPR 2022 and which got accepted as an oral. In this work, we propose a novel meshing algorithm for neural implicit representations, which uses learnt curvature information as part of the neural implicit representation in order to adapt generated triangle sizes to the underlying curvature.
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.
Authors
Mathias Vetsch, Sandro Lombardi, Marc Pollefeys and Martin R. Oswald
Venue
GCPR 2022, Konstanz