mlsanityData to 14 Oct 2026

TT-NF: Tensor Train Neural Fields

arXiv 2209.15529 ↗First posted 30 Sep 2022cs.LG cs.CV stat.MLcited by 7

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Abstract (arXiv)

Learning neural fields has been an active topic in deep learning research, focusing, among other issues, on finding more compact and easy-to-fit representations. In this paper, we introduce a novel low-rank representation termed Tensor Train Neural Fields (TT-NF) for learning neural fields on dense regular grids and efficient methods for sampling from them. Our representation is a TT parameterization of the neural field, trained with backpropagation to minimize a non-convex objective. We analyze the effect of low-rank compression on the downstream task quality metrics in two settings. First, we demonstrate the efficiency of our method in a sandbox task of tensor denoising, which admits comparison with SVD-based schemes designed to minimize reconstruction error. Furthermore, we apply the proposed approach to Neural Radiance Fields, where the low-rank structure of the field corresponding to the best quality can be discovered only through learning.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 33 of its 72 references that are in the map. First posted on arXiv 30 Sep 2022.

Builds on

33 of 72 references.

References · 33

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21 more
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Cited by year

Citing papers per year: 2022: 2; 2023: 4; 2024: 0; 2025: 1.

7 papers.

  1. 2022DiffDreamer: Towards Consistent Unsupervised Single-view Scene Extrapolation with Conditional Diffusion Modelscited by 51
  2. 2023Strivec: Sparse Tri-Vector Radiance Fieldscited by 28
  3. 2023Breathing New Life into 3D Assets with Generative Repaintingcited by 17
  4. 2022Towards Practical Control of Singular Values of Convolutional Layerscited by 9
  5. 2023Canonical Factors for Hybrid Neural Fieldscited by 8
  6. 2023Decomposition Ascribed Synergistic Learning for Unified Image Restorationcited by 3
  7. 2025Squeeze3D: Extreme Neural Compression with Latent Space Bridgingcited by 0