mlsanityData to 7 Oct 2026

Disorder-invariant Implicit Neural Representation

arXiv 2304.00837 ↗First posted 3 Apr 2023cs.CV eess.SPcited by 26

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Abstract (arXiv)

Implicit neural representation (INR) characterizes the attributes of a signal as a function of corresponding coordinates which emerges as a sharp weapon for solving inverse problems. However, the expressive power of INR is limited by the spectral bias in the network training. In this paper, we find that such a frequency-related problem could be greatly solved by re-arranging the coordinates of the input signal, for which we propose the disorder-invariant implicit neural representation (DINER) by augmenting a hash-table to a traditional INR backbone. Given discrete signals sharing the same histogram of attributes and different arrangement orders, the hash-table could project the coordinates into the same distribution for which the mapped signal can be better modeled using the subsequent INR network, leading to significantly alleviated spectral bias. Furthermore, the expressive power of the DINER is determined by the width of the hash-table. Different width corresponds to different geometrical elements in the attribute space, textite.g., 1D curve, 2D curved-plane and 3D curved-volume when the width is set as 1, 2 and 3, respectively. More covered areas of the geometrical elements result in stronger expressive power. Experiments not only reveal the generalization of the DINER for different INR backbones (MLP vs. SIREN) and various tasks (image/video representation, phase retrieval, refractive index recovery, and neural radiance field optimization) but also show the superiority over the state-of-the-art algorithms both in quality and speed. textitProject page: urlhttps://ezio77.github.io/DINER-website/

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). Builds on 34 of its 60 references that are in the map. First posted on arXiv 3 Apr 2023.

Builds on

34 of 60 references.

References · 34

  1. 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,698
  2. 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,682
  3. 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,214
  4. 2020Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domainscited by 2,170
  5. 2021Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fieldscited by 1,957
  6. 2021Plenoxels: Radiance Fields without Neural Networkscited by 1,607
  7. 2018On the Spectral Bias of Neural Networkscited by 1,321
  8. 2021Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstructioncited by 961
  9. 2020Neural Sparse Voxel Fieldscited by 959
  10. 2021PlenOctrees for Real-time Rendering of Neural Radiance Fieldscited by 904
  11. 2019Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelinescited by 672
  12. 2021Neural Geometric Level of Detail: Real-time Rendering with Implicit 3D Shapescited by 426
22 more
  1. 2020Local Implicit Grid Representations for 3D Scenescited by 372
  2. 2021Advances in Neural Renderingcited by 365
  3. 2021Light Field Networks: Neural Scene Representations with Single-Evaluation Renderingcited by 330
  4. 2020Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstructioncited by 321
  5. 2021NeRV: Neural Representations for Videoscited by 255
  6. 2019On the Inductive Bias of Neural Tangent Kernelscited by 177
  7. 2021BACON: Band-limited Coordinate Networks for Multiscale Scene Representationcited by 142
  8. 2019The Convergence Rate of Neural Networks for Learned Functions of Different Frequenciescited by 134
  9. 2021Neural Lumigraph Renderingcited by 105
  10. 2021A Structured Dictionary Perspective on Implicit Neural Representationscited by 100
  11. 2022ReLU Fields: The Little Non-linearity That Couldcited by 87
  12. 2022E-NeRV: Expedite Neural Video Representation with Disentangled Spatial-Temporal Contextcited by 76
  13. 2022ObjectFolder 2.0: A Multisensory Object Dataset for Sim2Real Transfercited by 73
  14. 2021Recovery of Continuous 3D Refractive Index Maps from Discrete Intensity-Only Measurements using Neural Fieldscited by 48
  15. 2020Compressive sensing with un-trained neural networks: Gradient descent finds the smoothest approximationcited by 39
  16. 2022DINER: Disorder-Invariant Implicit Neural Representationcited by 30
  17. 2022Scalable Neural Video Representations with Learnable Positional Featurescited by 29
  18. 2023Polynomial Neural Fields for Subband Decomposition and Manipulationcited by 22
  19. 2022PINs: Progressive Implicit Networks for Multi-Scale Neural Representationscited by 19
  20. 2021ImplicitVol: Sensorless 3D Ultrasound Reconstruction with Deep Implicit Representationcited by 16
  21. 2022Neural Residual Flow Fields for Efficient Video Representationscited by 16
  22. 2020Learning Unknown Physics of non-Newtonian Fluidscited by 13

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 9; 2025: 8; 2026: 7.

26 papers.

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  1. 2024From Chaos to Clarity: 3DGS in the Darkcited by 15
  2. 2024Nonparametric Teaching of Implicit Neural Representationscited by 13
  3. 2024FINER++: Building a Family of Variable-periodic Functions for Activating Implicit Neural Representationcited by 10
  4. 2023RHINO: Regularizing the Hash-based Implicit Neural Representationcited by 9
  5. 2024Inductive Gradient Adjustment For Spectral Bias In Implicit Neural Representationscited by 8
  6. 2025Optimizing Rank for High-Fidelity Implicit Neural Representationscited by 7
  7. 2023Continuous 3D Myocardial Motion Tracking via Echocardiographycited by 5
  8. 2025Continuous Representation Methods, Theories, and Applications: An Overview and Perspectivescited by 4
  9. 2024Towards the Spectral bias Alleviation by Normalizations in Coordinate Networkscited by 2
  10. 2024Predicting the Encoding Error of SIRENscited by 2
  11. 2025V2V3D: View-to-View Denoised 3D Reconstruction for Light-Field Microscopycited by 2
  12. 2025WIPES: Wavelet-based Visual Primitivescited by 2
14 more
  1. 2025Spectral Bottleneck in Sinusoidal Representation Networks: Noise is All You Needcited by 2
  2. 2024Neural Poisson Solver: A Universal and Continuous Framework for Natural Signal Blendingcited by 1
  3. 2024PNR: Physics-informed Neural Representation for high-resolution LFM reconstructioncited by 1
  4. 2025Self-supervised Deep Unrolled Model with Implicit Neural Representation Regularization for Accelerating MRI Reconstructioncited by 1
  5. 2026Content-Aware Frequency Encoding for Implicit Neural Representations with Fourier-Chebyshev Featurescited by 1
  6. 2024SL2A-INR: Single-Layer Learnable Activation for Implicit Neural Representationcited by 0
  7. 2025Rapid Whole Brain Motion-robust Mesoscale In-vivo MR Imaging using Multi-scale Implicit Neural Representationcited by 0
  8. 2025QuadINR: Hardware-Efficient Implicit Neural Representations Through Quadratic Activationcited by 0
  9. 2026The Inlet Rank Collapse in Implicit Neural Representations: Diagnosis and Unified Remedycited by 0
  10. 2026Adaptive Local Frequency Filtering for Fourier-Encoded Implicit Neural Representationscited by 0
  11. 2026Frequency-Decomposed INR for NIR-Assisted Low-Light RGB Image Denoisingcited by 0
  12. 2026Three-Step Conditional Diffusion 3D Reconstruction for Light-Field Microscopycited by 0
  13. 2026Texture-preserving implicit neural representation for Cone beam CT truncated reconstructioncited by 0
  14. 2026ASTRA: Asynchronous Spatio-Temporal Reconstruction via Trajectory Alignmentcited by 0