CasTensoRF: Cascaded Tensorial Radiance Fields for Novel View Synthesis

Wenpeng Xing, Jie Chen

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

Abstract

Novel views synthesized from Neural Radiance Fields (NeRF) have reached remarkable rendering quality. However, a 5D radiance field volume is too large to be stored or directly rendered. In order to efficiently reconstruct and manipulate such a high-order tensor, we leverage inspirations from previous tensor decomposition methods, e.g. Tensorial Radiance Fields (TensoRF) and Hierarchical Tucker decomposition. And we propose a Hierarchical Vector-Matrix decomposition (HVMD) framework to learn a sparse approximation of high-order tensors. The proposed HVMD takes advantage of tensor separation and factorization properties and builds a hierarchical scheme that enables a better approximation of the high-order tensor with a very limited number of parameters. Our method achieves better-rendering quality than TensoRF in the NeRF-synthetic dataset given the same model size. The advantage gets more significant when the network parameter number becomes extremely small.
Original languageEnglish
Title of host publication2023 IEEE International Conference on Multimedia and Expo (ICME)
PublisherIEEE
Pages2183-2188
Number of pages6
ISBN (Electronic)9781665468916
ISBN (Print)9781665468923
DOIs
Publication statusPublished - Jul 2023
Event2023 IEEE International Conference on Multimedia and Expo, ICME 2023 - Brisbane Convention & Exhibition Centre, Brisbane, Australia
Duration: 10 Jul 202314 Jul 2023
https://www.2023.ieeeicme.org/index.php
https://www.2023.ieeeicme.org/program.php
https://ieeexplore.ieee.org/xpl/conhome/10219544/proceeding

Publication series

NameIEEE International Conference on Multimedia and Expo (ICME)

Conference

Conference2023 IEEE International Conference on Multimedia and Expo, ICME 2023
Country/TerritoryAustralia
CityBrisbane
Period10/07/2314/07/23
Internet address

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