mlsanityData to 7 Oct 2026

Learning Spatially Collaged Fourier Bases for Implicit Neural Representation

arXiv 2312.17018 ↗First posted 28 Dec 2023cs.CV cs.LGcited by 11

Not on the map. Why?

Abstract (arXiv)

Existing approaches to Implicit Neural Representation (INR) can be interpreted as a global scene representation via a linear combination of Fourier bases of different frequencies. However, such universal basis functions can limit the representation capability in local regions where a specific component is unnecessary, resulting in unpleasant artifacts. To this end, we introduce a learnable spatial mask that effectively dispatches distinct Fourier bases into respective regions. This translates into collaging Fourier patches, thus enabling an accurate representation of complex signals. Comprehensive experiments demonstrate the superior reconstruction quality of the proposed approach over existing baselines across various INR tasks, including image fitting, video representation, and 3D shape representation. Our method outperforms all other baselines, improving the image fitting PSNR by over 3dB and 3D reconstruction to 98.81 IoU and 0.0011 Chamfer Distance.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 10 of its 15 references that are in the map. First posted on arXiv 28 Dec 2023.

Builds on

10 of 15 references.

References · 10

  1. 2017Attention Is All You Needcited by 59,932
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,682
  4. 2018Neural Tangent Kernel: Convergence and Generalization in Neural Networkscited by 2,597
  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. 2019Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representationscited by 864
  9. 2021A Structured Dictionary Perspective on Implicit Neural Representationscited by 100
  10. 2022Residual Multiplicative Filter Networks for Multiscale Reconstructioncited by 25

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 5; 2026: 3.

11 papers.

  1. 2024Nonparametric Teaching of Implicit Neural Representationscited by 13
  2. 2024ASMR: Activation-sharing Multi-resolution Coordinate Networks For Efficient Inferencecited by 9
  3. 2024H-SIREN: Improving implicit neural representations with hyperbolic periodic functionscited by 5
  4. 2025Continuous Representation Methods, Theories, and Applications: An Overview and Perspectivescited by 4
  5. 2025MetricGrids: Arbitrary Nonlinear Approximation with Elementary Metric Grids based Implicit Neural Representationcited by 2
  6. 2025NTK-Guided Implicit Neural Teachingcited by 2
  7. 2025MINR: Efficient Implicit Neural Representations for Multi-Image Encodingcited by 1
  8. 2025High-Frequency First: A Two-Stage Approach for Improving Image INRcited by 1
  9. 2026Content-Aware Frequency Encoding for Implicit Neural Representations with Fourier-Chebyshev Featurescited by 1
  10. 2026PatchINR: Patch-Based Implicit Neural Representations for Efficient and Scalable Inferencecited by 0
  11. 2026CARA: Collision-Aware Resolution Adaptation for Multiresolution Hash Encoding Based Image Fittingcited by 0