PREF: Phasorial Embedding Fields for Compact Neural Representations

• URL: http://arxiv.org/abs/2205.13524v1
• Date: Thu, 26 May 2022 17:43:03 GMT
• Title: PREF: Phasorial Embedding Fields for Compact Neural Representations
• Authors: Binbin Huang, Xinhao Yan, Anpei Chen, Shenghua Gao, Jingyi Yu
• Abstract summary: We present a phasorial embedding field emphPREF as a compact representation to facilitate neural signal modeling and reconstruction tasks. Our experiments show PREF-based neural signal processing technique is on par with the state-of-the-art in 2D image completion, 3D SDF surface regression, and 5D radiance field reconstruction.
• Score: 54.44527545923917
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We present a phasorial embedding field \emph{PREF} as a compact representation to facilitate neural signal modeling and reconstruction tasks. Pure multi-layer perceptron (MLP) based neural techniques are biased towards low frequency signals and have relied on deep layers or Fourier encoding to avoid losing details. PREF instead employs a compact and physically explainable encoding field based on the phasor formulation of the Fourier embedding space. We conduct a comprehensive theoretical analysis to demonstrate the advantages of PREF over the latest spatial embedding techniques. We then develop a highly efficient frequency learning framework using an approximated inverse Fourier transform scheme for PREF along with a novel Parseval regularizer. Extensive experiments show our compact PREF-based neural signal processing technique is on par with the state-of-the-art in 2D image completion, 3D SDF surface regression, and 5D radiance field reconstruction.

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