mlsanityData to 7 Oct 2026

Fluid Dynamics Network: Topology-Agnostic 4D Reconstruction via Fluid Dynamics Priors

arXiv 2303.09871 ↗First posted 17 Mar 2023cs.CV cs.GRcited by 7

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Abstract (arXiv)

Representing 3D surfaces as level sets of continuous functions over ℝ3 is the common denominator of neural implicit representations, which recently enabled remarkable progress in geometric deep learning and computer vision tasks. In order to represent 3D motion within this framework, it is often assumed (either explicitly or implicitly) that the transformations which a surface may undergo are homeomorphic: this is not necessarily true, for instance, in the case of fluid dynamics. In order to represent more general classes of deformations, we propose to apply this theoretical framework as regularizers for the optimization of simple 4D implicit functions (such as signed distance fields). We show that our representation is capable of capturing both homeomorphic and topology-changing deformations, while also defining correspondences over the continuously-reconstructed surfaces.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 31 of its 59 references that are in the map. First posted on arXiv 17 Mar 2023.

Builds on

31 of 59 references.

References · 31

  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. 2021NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstructioncited by 1,701
  8. 2020D-NeRF: Neural Radiance Fields for Dynamic Scenescited by 1,459
  9. 2020Nerfies: Deformable Neural Radiance Fieldscited by 1,194
  10. 2016A Point Set Generation Network for 3D Object Reconstruction from a Single Imagecited by 972
  11. 2020Convolutional Occupancy Networkscited by 760
  12. 2020Implicit Geometric Regularization for Learning Shapescited by 707
19 more
  1. 2019Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervisioncited by 699
  2. 2021UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstructioncited by 643
  3. 2020NeRV: Neural Reflectance and Visibility Fields for Relighting and View Synthesiscited by 493
  4. 2020Non-Rigid Neural Radiance Fields: Reconstruction and Novel View Synthesis of a Dynamic Scene From Monocular Videocited by 470
  5. 2020NeRD: Neural Reflectance Decomposition from Image Collectionscited by 446
  6. 2019SAL: Sign Agnostic Learning of Shapes from Raw Datacited by 389
  7. 2021SNARF: Differentiable Forward Skinning for Animating Non-Rigid Neural Implicit Shapescited by 221
  8. 2020Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solverscited by 201
  9. 2021Enhancing Computational Fluid Dynamics with Machine Learningcited by 137
  10. 2021Neural-GIF: Neural Generalized Implicit Functions for Animating People in Clothingcited by 102
  11. 2022MedNeRF: Medical Neural Radiance Fields for Reconstructing 3D-aware CT-Projections from a Single X-raycited by 66
  12. 2022CaDeX: Learning Canonical Deformation Coordinate Space for Dynamic Surface Representation via Neural Homeomorphismcited by 56
  13. 2022Physics Informed Neural Fields for Smoke Reconstruction with Sparse Datacited by 52
  14. 2021Learning Parallel Dense Correspondence from Spatio-Temporal Descriptors for Efficient and Robust 4D Reconstructioncited by 31
  15. 2019A Multi-Pass GAN for Fluid Flow Super-Resolutioncited by 14
  16. 2020Smoothed Particle Hydrodynamics Techniques for the Physics Based Simulation of Fluids and Solidscited by 9
  17. 2019Dynamic Upsampling of Smoke through Dictionary-based Learningcited by 8
  18. 2022KiloNeuS: A Versatile Neural Implicit Surface Representation for Real-Time Renderingcited by 4
  19. 2021Ships, Splashes, and Waves on a Vast Oceancited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 3; 2026: 1.

7 papers.

  1. 2023NVFi: Neural Velocity Fields for 3D Physics Learning from Dynamic Videoscited by 28
  2. 2025FreeGave: 3D Physics Learning from Dynamic Videos by Gaussian Velocitycited by 13
  3. 2025TRACE: Learning 3D Gaussian Physical Dynamics from Multi-view Videoscited by 11
  4. 2024DynoSurf: Neural Deformation-based Temporally Consistent Dynamic Surface Reconstructioncited by 8
  5. 2025Dynamic Neural Surfaces for Elastic 4D Shape Representation and Analysiscited by 5
  6. 2024Regularizing Dynamic Radiance Fields with Kinematic Fieldscited by 4
  7. 2026Accelerated Spatio-Temporal Gaussian Rendering via Kinematic and Semantic Priorscited by 0