Question: Which method is the slowest among the comparison methods in the paper 'MeshFeat: Multi-Resolution Features for Neural Fields on Meshes'? Choose one from the figure below.
Date: 2024-07-19

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MeshFeat: Multi-Resolution Features for Neural Fields on Meshes
View PDF Abstract: Parametric feature grid encodings have gained significant attention as an encoding approach for neural fields since they allow for much smaller MLPs, which significantly decreases the inference time of the models. In this work, we propose MeshFeat, a parametric feature encoding tailored to meshes, for which we adapt the idea of multi-resolution feature grids from Euclidean ...

MeshFeat: Multi-Resolution Features for Neural Fields on Meshes
We start from the structure provided by the given vertex topology and use a mesh simplification algorithm to construct a multi-resolution feature representation directly on the mesh. The approach allows the usage of small MLPs for neural fields on meshes, and we show a significant speed-up compared to previous representations while maintaining ...

MeshFeat: Multi-Resolution Features for Neural Fields on Meshes | arXiv.org
1 Introduction Figure 1: We present MeshFeat, a parametric encoding strategy for neural fields on meshes. We propose a multi-resolution strategy based on mesh simplification to enhance the efficiency of the encoding. Our approach allows for much smaller MLPs than previous frequency-based encodings, resulting in significantly faster inference times.

MeshFeat: Multi-Resolution Features for Neural Fields on Meshes
MeshFeat: Multi-Resolution Features for Neural Fields on Meshes (M Mahajan, F Hofherr and D Cremers), In European Conference on Computer Vision (ECCV), 2024.

Neural Geometry Fields for Meshes | iveevi.github.io
We present Neural Geometry Fields, a neural representation for discrete surface geometry represented by triangle meshes. Our idea is to represent the target surface using a coarse set of quadrangular patches, and add surface details using coordinate neural networks by displacing the patches. We then extract a traditional triangular mesh from a ...

MeshFeat: Multi-Resolution Features for Neural Fields on Meshes
Parametric feature grid encodings have gained significant attention as an encoding approach for neural fields since they allow for much smaller MLPs, which significantly decreases the inference time of the models. In this work, we propose MeshFeat, a parametric feature encoding tailored to meshes, for which we adapt the idea of multi-resolution feature grids from Euclidean space.

[2402.10365] Deep Spectral Meshes: Multi-Frequency Facial Mesh ...
In this work, spectral meshes are introduced as a method to decompose mesh deformations into low-frequency and high-frequency deformations. These features of low- and high-frequency deformations are used for representation learning with graph convolutional networks. A parametric model for 3D facial mesh synthesis is built upon the proposed ...

[2402.08863] Multiscale graph neural networks with adaptive mesh ...
Mesh-based Graph Neural Networks (GNNs) have recently shown capabilities to simulate complex multiphysics problems with accelerated performance times. However, mesh-based GNNs require a large number of message-passing (MP) steps and suffer from over-smoothing for problems involving very fine mesh. In this work, we develop a multiscale mesh-based GNN framework mimicking a conventional iterative ...

Answer: NeuTex