mlsanityData to 7 Oct 2026

MeshFeat: Multi-Resolution Features for Neural Fields on Meshes

arXiv 2407.13592 ↗First posted 18 Jul 2024cs.CV cs.LGcited by 3

Not on the map. Why?

Abstract (arXiv)

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. 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 comparable reconstruction quality for texture reconstruction and BRDF representation. Given its intrinsic coupling to the vertices, the method is particularly well-suited for representations on deforming meshes, making it a good fit for object animation.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 37 of its 52 references that are in the map. First posted on arXiv 18 Jul 2024.

Builds on

37 of 52 references.

References · 37

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  3. 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,698
  4. 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,682
  5. 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,214
  6. 2020Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domainscited by 2,170
  7. 2018Occupancy Networks: Learning 3D Reconstruction in Function Spacecited by 2,037
  8. 2021NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstructioncited by 1,701
  9. 2021Plenoxels: Radiance Fields without Neural Networkscited by 1,607
  10. 2018On the Spectral Bias of Neural Networkscited by 1,321
  11. 2018Learning Implicit Fields for Generative Shape Modelingcited by 1,060
  12. 2021Volume Rendering of Neural Implicit Surfacescited by 990
25 more
  1. 2021Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstructioncited by 961
  2. 2020Neural Sparse Voxel Fieldscited by 959
  3. 2020Convolutional Occupancy Networkscited by 760
  4. 2020Implicit Geometric Regularization for Learning Shapescited by 707
  5. 2023Neuralangelo: High-Fidelity Neural Surface Reconstructioncited by 464
  6. 2021Neural Geometric Level of Detail: Real-time Rendering with Implicit 3D Shapescited by 426
  7. 2020Local Implicit Grid Representations for 3D Scenescited by 372
  8. 2021NeRF in the Dark: High Dynamic Range View Synthesis from Noisy Raw Imagescited by 336
  9. 2021Text2Mesh: Text-Driven Neural Stylization for Meshescited by 332
  10. 2020Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstructioncited by 321
  11. 2019Texture Fields: Learning Texture Representations in Function Spacecited by 210
  12. 2022NeuMesh: Learning Disentangled Neural Mesh-based Implicit Field for Geometry and Texture Editingcited by 141
  13. 2020Frequency Bias in Neural Networks for Input of Non-Uniform Densitycited by 140
  14. 2021Modulated Periodic Activations for Generalizable Local Functional Representationscited by 140
  15. 2021NeuTex: Neural Texture Mapping for Volumetric Neural Renderingcited by 98
  16. 2021SAPE: Spatially-Adaptive Progressive Encoding for Neural Optimizationcited by 59
  17. 2020Multi-View Photometric Stereo: A Robust Solution and Benchmark Dataset for Spatially Varying Isotropic Materialscited by 56
  18. 2022Generalised Implicit Neural Representationscited by 31
  19. 2022Intrinsic Neural Fields: Learning Functions on Manifoldscited by 20
  20. 2023Text2Scene: Text-driven Indoor Scene Stylization with Part-aware Detailscited by 18
  21. 2023Explicit Neural Surfaces: Learning Continuous Geometry With Deformation Fieldscited by 10
  22. 2023Leveraging Intrinsic Properties for Non-Rigid Garment Alignmentcited by 6
  23. 2021Surface Multigrid via Intrinsic Prolongationcited by 4
  24. 2021Mesh Draping: Parametrization-Free Neural Mesh Transfercited by 4
  25. 2024Hybrid Neural Representations for Spherical Datacited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 1.

3 papers.

  1. 2025Vertex Features for Neural Global Illuminationcited by 3
  2. 2026Heat Kernel Textures: the Geodesic Gaussians That Do Not Splatcited by 1
  3. 2024Neural Image Unfolding: Flattening Sparse Anatomical Structures using Neural Fieldscited by 0