Neural fourier filter bank
department or school: Science, Faculty of
department or school: Computer Science, Department of
data provider: DSpace
degree campus: UBCV
creator: Wu, Zhijie
issued: 2023-08-22T15:37:50Z
issued: 2023
related degree: Master of Science - MSc
degree grantor: University of British Columbia
description: We present a novel method to provide efficient and highly detailed reconstructions. Inspired by wavelets, we learn a neural field that decompose the signal both spatially and frequency-wise. We follow the recent grid-based paradigm for spatial decomposition, but unlike existing work, encourage specific frequencies to be stored in each grid via Fourier features encodings. We then apply a multi-layer perceptron with sine activations, taking these Fourier encoded features in at appropriate layers so that higher-frequency components are accumulated on top of lower-frequency components sequentially, which we sum up to form the final output. We demonstrate that our method outperforms the state of the art regarding model compactness and convergence speed on multiple tasks: 2D image fitting, 3D shape reconstruction, and neural radiance fields. Our code is available at https://github.com/ubc-vision/NFFB.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/85566
note: Neural Fourier Filter BankbyZhijie WuB.Sc., Sichuan University, 2016A THESIS SUBMITTED IN PARTIAL FULFILLMENTOF THE REQUIREMENTS FOR THE DEGREE OFMaster of ScienceinTHE FACULTY OF GRADUATE AND POSTDOCTORALSTUDIES(Computer Science)The University of British Columbia(Vancouver)August 2023© Zhijie Wu, 2023The following individuals certify that they have read, and recommend to the Fac-ulty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled:Neural Fourier Filter Banksubmitted by Zhijie Wu in partial fulfillment of the requirements for the degree ofMaster of Science in Computer Science.Examining Committee:Kwang Moo Yi, Assistant Professor, Computer Science, UBCSupervisorVered Shwartz, Assistant Professor, Computer Science, UBCSupervisory Committee MemberiiAbstractWe present a novel method to provide efficient and highly detailed reconstruc-tions. Inspired by wavelets, we learn a neural field that decompose the signalboth spatially and frequency-wise. We follow the recent grid-based paradigm forspatial decomposition, but unlike existing work, encourage specific frequenciesto be stored in each grid via Fourier features encodings. We then apply a multi-layer perceptron with sine activations, taking these Fourier encoded features in atappropriate layers so that higher-frequency components are accumulated on topof lower-frequency components sequentially, which we sum up to form the finaloutput. We demonstrate that our method outperforms the state of the art regard-ing model compactness and convergence speed on multiple tasks: 2D image fit-ting, 3D shape reconstruction, and neural radiance fields. Our code is available athttps://github.com/ubc-vision/NFFB.iiiLay SummaryIn this thesis, we introduce a novel artificial neural network tailored to synthe-size high-quality results while minimizing computation budgets and maintainingdesirable method complexities. Our motivation stems from observing that sig-nals exhibit varying frequency changes across spatial reg…
has type: Thesis/Dissertation
date issued: 2023-11
is shown at: 10.14288/1.0435529
language: eng
degree discipline: Computer Science
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Yi, Kwang Moo
type: Text
identifier URI: http://hdl.handle.net/2429/85566