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Multilayer perceptrons (MLPs) learn high frequencies slowly. Recentapproaches encode features in spatial bins to improve speed of learningdetails, 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 commonlyused for positional encoding. We call these Quantized Fourier Features (QFF).As a naturally multiresolution and periodic representation, our experimentsshow that using QFF can result in smaller model size, faster training, andbetter quality outputs for several applications, including Neural ImageRepresentations (NIR), Neural Radiance Field (NeRF) and Signed DistanceFunction (SDF) modeling. QFF are easy to code, fast to compute, and serve as asimple drop-in addition to many neural field representations.

QFF: Quantized Fourier Features for Neural Field Representations