mlsanityData to 14 Oct 2026

Neural Space-filling Curves

arXiv 2204.08453 ↗First posted 18 Apr 2022cs.CVcited by 3

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Abstract (arXiv)

We present Neural Space-filling Curves (SFCs), a data-driven approach to infer a context-based scan order for a set of images. Linear ordering of pixels forms the basis for many applications such as video scrambling, compression, and auto-regressive models that are used in generative modeling for images. Existing algorithms resort to a fixed scanning algorithm such as Raster scan or Hilbert scan. Instead, our work learns a spatially coherent linear ordering of pixels from the dataset of images using a graph-based neural network. The resulting Neural SFC is optimized for an objective suitable for the downstream task when the image is traversed along with the scan line order. We show the advantage of using Neural SFCs in downstream applications such as image compression. Code and additional results will be made available at https://hywang66.github.io/publication/neuralsfc.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 15 of its 46 references that are in the map. First posted on arXiv 18 Apr 2022.

Builds on

15 of 46 references.

References · 15

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2017Graph Attention Networkscited by 8,923
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  8. 2019Generating Diverse High-Fidelity Images with VQ-VAE-2cited by 1,387
  9. 2015Pointer Networkscited by 895
  10. 2018Attention, Learn to Solve Routing Problems!cited by 656
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  12. 2021NeRV: Neural Representations for Videoscited by 256
3 more
  1. 2015Order Matters: Sequence to sequence for setscited by 237
  2. 2016TGIF: A New Dataset and Benchmark on Animated GIF Descriptioncited by 124
  3. 2020Data-Driven Space-Filling Curvescited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025Machine learning for modelling unstructured grid data in computational physics: a reviewcited by 12
  2. 2025LOOPE: Learnable Optimal Patch Order in Positional Embeddings for Vision Transformerscited by 0
  3. 2026Spatial Priors via Space Filling Curves for Small and Limited Data Vision Transformerscited by 0