QFF: Quantized Fourier Features for Neural Field Representations

12/02/2022
by   Jae Yong Lee, et al.
0

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.

READ FULL TEXT

page 1

page 5

page 6

page 7

page 8

research
02/04/2021

Wind Field Reconstruction with Adaptive Random Fourier Features

We investigate the use of spatial interpolation methods for reconstructi...
research
09/11/2023

Quantized Fourier and Polynomial Features for more Expressive Tensor Network Models

In the context of kernel machines, polynomial and Fourier features are c...
research
05/26/2022

PREF: Phasorial Embedding Fields for Compact Neural Representations

We present a phasorial embedding field PREF as a compact representation ...
research
11/29/2022

Fourier-Net: Fast Image Registration with Band-limited Deformation

Unsupervised image registration commonly adopts U-Net style networks to ...
research
03/27/2018

Periodic Fourier representation of boolean functions

In this work, we consider a new type of Fourier-like representation of b...
research
09/01/2021

Seeing Implicit Neural Representations as Fourier Series

Implicit Neural Representations (INR) use multilayer perceptrons to repr...
research
04/14/2020

Breaking the waves: asymmetric random periodic features for low-bitrate kernel machines

Many signal processing and machine learning applications are built from ...

Please sign up or login with your details

Forgot password? Click here to reset