mlsanityData to 14 Oct 2026

Where Do We Stand with Implicit Neural Representations? A Technical and Performance Survey

arXiv 2411.03688 ↗First posted 6 Nov 2024cs.CVcited by 42

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Abstract (arXiv)

Implicit Neural Representations (INRs) have emerged as a paradigm in knowledge representation, offering exceptional flexibility and performance across a diverse range of applications. INRs leverage multilayer perceptrons (MLPs) to model data as continuous implicit functions, providing critical advantages such as resolution independence, memory efficiency, and generalisation beyond discretised data structures. Their ability to solve complex inverse problems makes them particularly effective for tasks including audio reconstruction, image representation, 3D object reconstruction, and high-dimensional data synthesis. This survey provides a comprehensive review of state-of-the-art INR methods, introducing a clear taxonomy that categorises them into four key areas: activation functions, position encoding, combined strategies, and network structure optimisation. We rigorously analyse their critical properties, such as full differentiability, smoothness, compactness, and adaptability to varying resolutions while also examining their strengths and limitations in addressing locality biases and capturing fine details. Our experimental comparison offers new insights into the trade-offs between different approaches, showcasing the capabilities and challenges of the latest INR techniques across various tasks. In addition to identifying areas where current methods excel, we highlight key limitations and potential avenues for improvement, such as developing more expressive activation functions, enhancing positional encoding mechanisms, and improving scalability for complex, high-dimensional data. This survey serves as a roadmap for researchers, offering practical guidance for future exploration in the field of INRs. We aim to foster new methodologies by outlining promising research directions for INRs and applications.

Key facts. Cited by 42 papers in the map (data through 14 Oct 2026). Builds on 37 of its 45 references that are in the map. First posted on arXiv 6 Nov 2024.

Builds on

37 of 45 references.

References · 37

  1. 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,689
  2. 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,238
  3. 2020Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domainscited by 2,200
  4. 2018Occupancy Networks: Learning 3D Reconstruction in Function Spacecited by 2,041
  5. 2018On the Spectral Bias of Neural Networkscited by 1,341
  6. 2018Learning Implicit Fields for Generative Shape Modelingcited by 1,060
  7. 2016A Point Set Generation Network for 3D Object Reconstruction from a Single Imagecited by 979
  8. 2018Pixel2Mesh: Generating 3D Mesh Models from Single RGB Imagescited by 651
  9. 2020Learning Continuous Image Representation with Local Implicit Image Functioncited by 552
  10. 2021Neural Fields in Visual Computing and Beyondcited by 540
  11. 2020Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstructioncited by 321
  12. 2018Learning Category-Specific Mesh Reconstruction from Image Collectionscited by 317
25 more
  1. 2018Generating 3D faces using Convolutional Mesh Autoencoderscited by 268
  2. 2023WIRE: Wavelet Implicit Neural Representationscited by 220
  3. 2019DIST: Rendering Deep Implicit Signed Distance Function with Differentiable Sphere Tracingcited by 180
  4. 2019Deep ReLU Networks Have Surprisingly Few Activation Patternscited by 161
  5. 2021Beyond Periodicity: Towards a Unifying Framework for Activations in Coordinate-MLPscited by 135
  6. 2018Recent Advances in Autoencoder-Based Representation Learningcited by 133
  7. 20163D Shape Induction from 2D Views of Multiple Objectscited by 121
  8. 2023Implicit Neural Representation in Medical Imaging: A Comparative Surveycited by 90
  9. 2022Transformers as Meta-Learners for Implicit Neural Representationscited by 79
  10. 2023FINER: Flexible spectral-bias tuning in Implicit NEural Representation by Variable-periodic Activation Functionscited by 78
  11. 2016Unsupervised Learning of 3D Structure from Imagescited by 67
  12. 2022Signal Processing for Implicit Neural Representationscited by 46
  13. 2021IREM: High-Resolution Magnetic Resonance (MR) Image Reconstruction via Implicit Neural Representationcited by 42
  14. 2018The Sparse Manifold Transformcited by 36
  15. 2024Improved Implicit Neural Representation with Fourier Reparameterized Trainingcited by 36
  16. 2023INCODE: Implicit Neural Conditioning with Prior Knowledge Embeddingscited by 31
  17. 2024A Sampling Theory Perspective on Activations for Implicit Neural Representationscited by 24
  18. 2021A Multi-Implicit Neural Representation for Fontscited by 21
  19. 2024HOSC: A Periodic Activation Function for Preserving Sharp Features in Implicit Neural Representationscited by 14
  20. 2020K-Deep Simplex: Deep Manifold Learning via Local Dictionariescited by 8
  21. 2024Biophysics Informed Pathological Regularisation for Brain Tumour Segmentationcited by 7
  22. 2024MicroDiffusion: Implicit Representation-Guided Diffusion for 3D Reconstruction from Limited 2D Microscopy Projectionscited by 6
  23. 2024ReLUs Are Sufficient for Learning Implicit Neural Representationscited by 5
  24. 2023TRIDENT: The Nonlinear Trilogy for Implicit Neural Representationscited by 3
  25. 2023Single-Shot Plug-and-Play Methods for Inverse Problemscited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 20; 2026: 21.

42 papers.

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  1. 2025Optimizing Rank for High-Fidelity Implicit Neural Representationscited by 8
  2. 2026Molecular Representations in Implicit Functional Space via Hyper-Networkscited by 5
  3. 2024Unsupervised Multi-Parameter Inverse Solving for Reducing Ring Artifacts in 3D X-Ray CBCTcited by 4
  4. 2025How Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodingscited by 3
  5. 2025SDFs from Unoriented Point Clouds using Neural Variational Heat Distancescited by 2
  6. 2025FM-SIREN & FM-FINER: Implicit Neural Representation Using Nyquist-based Orthogonalitycited by 2
  7. 2025Geodesic Calculus on Implicitly Defined Latent Manifoldscited by 2
  8. 2026Location Is All You Need: Continuous Spatiotemporal Neural Representations of Earth Observation Datacited by 2
  9. 2025Deep Spectral Priorcited by 1
  10. 2025Physics-Guided Dual Implicit Neural Representations for Source Separationcited by 1
  11. 2025Flows and Diffusions on the Neural Manifoldcited by 1
  12. 2025cIDIR: Conditioned Implicit Neural Representation for Regularized Deformable Image Registrationcited by 1
30 more
  1. 2025TabINR: An Implicit Neural Representation Framework for Tabular Data Imputationcited by 1
  2. 2025Weight Space Representation Learning via Neural Field Adaptationcited by 1
  3. 2025SuperF: Neural Implicit Fields for Multi-Image Super-Resolutioncited by 1
  4. 2026WSI-INR: Implicit Neural Representations for Lesion Segmentation in Whole-Slide Imagescited by 1
  5. 2026ShuffleFlow: Scalable Posterior Inference for Bayesian Inverse Imagingcited by 1
  6. 2025Implicit Neural Representations of Molecular Vector-Valued Functionscited by 0
  7. 2025Impilict Runge-Kutta based sparse identification of governing equations in biologically motivated systemscited by 0
  8. 2025Model-based Implicit Neural Representation for sub-wavelength Radio Localizationcited by 0
  9. 2025T-MLP: Tailed Multi-Layer Perceptron for Level-of-Detail Signal Representationcited by 0
  10. 2025Interlaced dynamic XCT reconstruction with spatio-temporal implicit neural representationscited by 0
  11. 2025Semi-disentangled spatiotemporal implicit neural representations of longitudinal neuroimaging data for trajectory classificationcited by 0
  12. 2025Three-dimensional inversion of gravity data using implicit neural representations and scientific machine learningcited by 0
  13. 2025In Situ Training of Implicit Neural Compressors for Scientific Simulations via Sketch-Based Regularizationcited by 0
  14. 2026Data-Efficient Meningioma Segmentation via Implicit Spatiotemporal Mixing and Sim2Real Semantic Injectioncited by 0
  15. 2026Gauss-Newton Natural Gradient Descent for Shape Learningcited by 0
  16. 2026Escaping Spectral Bias without Backpropagation: Fast Implicit Neural Representations with Extreme Learning Machinescited by 0
  17. 2026Refine Now, Query Fast: A Decoupled Refinement Paradigm for Implicit Neural Fieldscited by 0
  18. 2026Universal Hypernetworks for Arbitrary Modelscited by 0
  19. 2026Adaptive Local Frequency Filtering for Fourier-Encoded Implicit Neural Representationscited by 0
  20. 2026Medial Axis Aware Learning of Signed Distance Functionscited by 0
  21. 2026Frequency-Decomposed INR for NIR-Assisted Low-Light RGB Image Denoisingcited by 0
  22. 2026Render, Don't Decode: Weight-Space World Models with Latent Structural Disentanglementcited by 0
  23. 2026AIR: Amortized Image Reconstruction Framework for Self-Supervised Feed-Forward 2D Gaussian Splattingcited by 0
  24. 2026ImplicitTerrainV2: Wavelet-Guided Spatially Adaptive Neural Terrain Representationcited by 0
  25. 2026A Principled Self-Referenced Early Stopping Approach for Deep Image Priorcited by 0
  26. 2026Jacobi-Anger Method for Deterministic Initialization in Implicit Neural Representationcited by 0
  27. 2026Architecture Shapes Transfer Specificity in Implicit Neural Representationscited by 0
  28. 2026PRIME-SVR: Physics-infoRmed Implicit Multi-Echo Slice-to-Volume Reconstruction for Fetal T2 mappingcited by 0
  29. 2026Weight-Space Mixture-of-Experts for Implicit Neural Representation Classificationcited by 0
  30. 2026Neural Field Ensembles for Aerodynamic Surface Prediction: Winning Solution to the ONERA CRM Wall Distribution 2025 Challengecited by 0