mlsanityData to 7 Oct 2026

QFF: Quantized Fourier Features for Neural Field Representations

arXiv 2212.00914 ↗First posted 2 Dec 2022cs.CVcited by 2

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Abstract (arXiv)

Multilayer perceptrons (MLPs) learn high frequencies slowly. Recent approaches encode features in spatial bins to improve speed of learning details, but at the cost of larger model size and loss of continuity. Instead, we propose to encode features in bins of Fourier features that are commonly used for positional encoding. We call these Quantized Fourier Features (QFF). As a naturally multiresolution and periodic representation, our experiments show that using QFF can result in smaller model size, faster training, and better quality outputs for several applications, including Neural Image Representations (NIR), Neural Radiance Field (NeRF) and Signed Distance Function (SDF) modeling. QFF are easy to code, fast to compute, and serve as a simple drop-in addition to many neural field representations.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 36 of its 41 references that are in the map. First posted on arXiv 2 Dec 2022.

Builds on

36 of 41 references.

References · 36

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24 more
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Cited by year

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

2 papers.

  1. 2023Factor Fields: A Unified Framework for Neural Fields and Beyondcited by 58
  2. 2024MonoPatchNeRF: Improving Neural Radiance Fields with Patch-based Monocular Guidancecited by 2