mlsanityData to 7 Oct 2026

VisCo Grids: Surface Reconstruction with Viscosity and Coarea Grids

arXiv 2303.14569 ↗First posted 25 Mar 2023cs.CVcited by 20

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Abstract (arXiv)

Surface reconstruction has been seeing a lot of progress lately by utilizing Implicit Neural Representations (INRs). Despite their success, INRs often introduce hard to control inductive bias (i.e., the solution surface can exhibit unexplainable behaviours), have costly inference, and are slow to train. The goal of this work is to show that replacing neural networks with simple grid functions, along with two novel geometric priors achieve comparable results to INRs, with instant inference, and improved training times. To that end we introduce VisCo Grids: a grid-based surface reconstruction method incorporating Viscosity and Coarea priors. Intuitively, the Viscosity prior replaces the smoothness inductive bias of INRs, while the Coarea favors a minimal area solution. Experimenting with VisCo Grids on a standard reconstruction baseline provided comparable results to the best performing INRs on this dataset.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 26 of its 46 references that are in the map. First posted on arXiv 25 Mar 2023.

Builds on

26 of 46 references.

References · 26

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,698
  3. 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,682
  4. 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,214
  5. 2020Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domainscited by 2,170
  6. 2018Occupancy Networks: Learning 3D Reconstruction in Function Spacecited by 2,037
  7. 2021Plenoxels: Radiance Fields without Neural Networkscited by 1,607
  8. 2018Learning Implicit Fields for Generative Shape Modelingcited by 1,060
  9. 2021Volume Rendering of Neural Implicit Surfacescited by 990
  10. 2020Implicit Geometric Regularization for Learning Shapescited by 707
  11. 2018AtlasNet: A Papier-Mach'e Approach to Learning 3D Surface Generationcited by 539
  12. 2021Neural Geometric Level of Detail: Real-time Rendering with Implicit 3D Shapescited by 426
14 more
  1. 2019SAL: Sign Agnostic Learning of Shapes from Raw Datacited by 389
  2. 2020Local Implicit Grid Representations for 3D Scenescited by 372
  3. 2021Shape As Points: A Differentiable Poisson Solvercited by 186
  4. 2021BACON: Band-limited Coordinate Networks for Multiscale Scene Representationcited by 142
  5. 2020Points2Surf: Learning Implicit Surfaces from Point Cloud Patchescited by 122
  6. 2020SALD: Sign Agnostic Learning with Derivativescited by 115
  7. 2018Deep Geometric Prior for Surface Reconstructioncited by 110
  8. 2020ParSeNet: A Parametric Surface Fitting Network for 3D Point Cloudscited by 96
  9. 2021DiGS : Divergence guided shape implicit neural representation for unoriented point cloudscited by 76
  10. 2021Deep Implicit Moving Least-Squares Functions for 3D Reconstructioncited by 67
  11. 2020Neural Splines: Fitting 3D Surfaces with Infinitely-Wide Neural Networkscited by 52
  12. 2022Surface Reconstruction from Point Clouds: A Survey and a Benchmarkcited by 41
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  14. 2020Learning Delaunay Surface Elements for Mesh Reconstructioncited by 23

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 7; 2024: 10; 2025: 2; 2026: 1.

20 papers.

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  1. 2023Towards Better Gradient Consistency for Neural Signed Distance Functions via Level Set Alignmentcited by 39
  2. 2023Learning Signed Distance Functions from Noisy 3D Point Clouds via Noise to Noise Mappingcited by 27
  3. 2024NeuRodin: A Two-stage Framework for High-Fidelity Neural Surface Reconstructioncited by 16
  4. 2024Fast Learning of Signed Distance Functions from Noisy Point Clouds via Noise to Noise Mappingcited by 15
  5. 2023GridPull: Towards Scalability in Learning Implicit Representations from 3D Point Cloudscited by 11
  6. 2024MultiPull: Detailing Signed Distance Functions by Pulling Multi-Level Queries at Multi-Stepcited by 11
  7. 2023Progressive Learning for Physics-informed Neural Motion Planningcited by 8
  8. 2023p-Poisson surface reconstruction in curl-free flow from point cloudscited by 8
  9. 2024HotSpot: Signed Distance Function Optimization with an Asymptotically Sufficient Conditioncited by 8
  10. 2024Implicit Filtering for Learning Neural Signed Distance Functions from 3D Point Cloudscited by 7
  11. 2025Learning Bijective Surface Parameterization for Inferring Signed Distance Functions from Sparse Point Clouds with Grid Deformationcited by 7
  12. 2024GS-Octree: Octree-based 3D Gaussian Splatting for Robust Object-level 3D Reconstruction Under Strong Lightingcited by 6
8 more
  1. 2023InstantAvatar: Efficient 3D Head Reconstruction via Surface Renderingcited by 4
  2. 2024Self-Supervised Dual Contouringcited by 2
  3. 2024NumGrad-Pull: Numerical Gradient Guided Tri-plane Representation for Surface Reconstruction from Point Cloudscited by 2
  4. 2024Freeplane: Unlocking Free Lunch in Triplane-Based Sparse-View Reconstruction Modelscited by 1
  5. 2023Enhancing Neural Rendering Methods with Image Augmentationscited by 0
  6. 2024Mat'ern Kernels for Tunable Implicit Surface Reconstructioncited by 0
  7. 2025Neural Shortest Path for Surface Reconstruction from Point Cloudscited by 0
  8. 2026Points as Tori: Fast Pointwise Signed Distance for Point Cloudscited by 0