mlsanityData to 7 Oct 2026

Neural Gaussian Scale-Space Fields

arXiv 2405.20980 ↗First posted 31 May 2024cs.CV cs.GR cs.LGcited by 6

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Abstract (arXiv)

Gaussian scale spaces are a cornerstone of signal representation and processing, with applications in filtering, multiscale analysis, anti-aliasing, and many more. However, obtaining such a scale space is costly and cumbersome, in particular for continuous representations such as neural fields. We present an efficient and lightweight method to learn the fully continuous, anisotropic Gaussian scale space of an arbitrary signal. Based on Fourier feature modulation and Lipschitz bounding, our approach is trained self-supervised, i.e., training does not require any manual filtering. Our neural Gaussian scale-space fields faithfully capture multiscale representations across a broad range of modalities, and support a diverse set of applications. These include images, geometry, light-stage data, texture anti-aliasing, and multiscale optimization.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 41 of its 98 references that are in the map. First posted on arXiv 31 May 2024.

Builds on

41 of 98 references.

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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 2.

6 papers.

  1. 2025VertexRegen: Mesh Generation with Continuous Level of Detailcited by 14
  2. 2026Neural Harmonic Textures for High-Quality Primitive Based Neural Reconstructioncited by 2
  3. 2025T-MLP: Tailed Multi-Layer Perceptron for Level-of-Detail Signal Representationcited by 0
  4. 2025Learning Neural Antiderivativescited by 0
  5. 2025Spectral Prefiltering of Neural Fieldscited by 0
  6. 2026HOSC: A Periodic Activation with Saturation Control for High-Fidelity Implicit Neural Representationscited by 0