mlsanityData to 14 Oct 2026

Neural Field Convolutions by Repeated Differentiation

arXiv 2304.01834 ↗First posted 4 Apr 2023cs.CV cs.GRcited by 9

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Abstract (arXiv)

Neural fields are evolving towards a general-purpose continuous representation for visual computing. Yet, despite their numerous appealing properties, they are hardly amenable to signal processing. As a remedy, we present a method to perform general continuous convolutions with general continuous signals such as neural fields. Observing that piecewise polynomial kernels reduce to a sparse set of Dirac deltas after repeated differentiation, we leverage convolution identities and train a repeated integral field to efficiently execute large-scale convolutions. We demonstrate our approach on a variety of data modalities and spatially-varying kernels.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 33 of its 69 references that are in the map. First posted on arXiv 4 Apr 2023.

Builds on

33 of 69 references.

References · 33

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21 more
  1. 2021Advances in Neural Renderingcited by 365
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Cited by year

Citing papers per year: 2023: 1; 2024: 4; 2025: 4.

9 papers.

  1. 2024Neural Geometry Processing via Spherical Neural Surfacescited by 14
  2. 2025Stochastic Preconditioning for Neural Field Optimizationcited by 8
  3. 2024Neural Gaussian Scale-Space Fieldscited by 6
  4. 2024Neural Control Variates with Automatic Integrationcited by 5
  5. 2024Towards Croppable Implicit Neural Representationscited by 5
  6. 2025Learnable Fractional Reaction-Diffusion Dynamics for Under-Display ToF Imaging and Beyondcited by 1
  7. 2023Neural Boundingcited by 0
  8. 2025Learning Neural Antiderivativescited by 0
  9. 2025Spectral Prefiltering of Neural Fieldscited by 0