QFF: Quantized Fourier Features for Neural Field Representations

• URL: http://arxiv.org/abs/2212.00914v1
• Date: Fri, 2 Dec 2022 00:11:22 GMT
• Title: QFF: Quantized Fourier Features for Neural Field Representations
• Authors: Jae Yong Lee, Yuqun Wu, Chuhang Zou, Shenlong Wang, Derek Hoiem
• Abstract summary: We show that using Quantized Fourier Features (QFF) can result in smaller model size, faster training, and better quality outputs for several applications. QFF are easy to code, fast to compute, and serve as a simple drop-in addition to many neural field representations.
• Score: 28.82293263445964
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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