Colorizing Monochromatic Radiance Fields

Yean Cheng, Renjie Wan*, Shuchen Weng, Chengxuan Zhu, Yakun Chang, Boxin Shi

*Corresponding author for this work

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

Abstract

Though Neural Radiance Fields (NeRF) can produce colorful 3D representations of the world by using a set of 2D images, such ability becomes non-existent when only monochromatic images are provided. Since color is necessary in representing the world, reproducing color from monochromatic radiance fields becomes crucial. To achieve this goal, instead of manipulating the monochromatic radiance fields directly, we consider it as a representation-prediction task in the Lab color space. By first constructing the luminance and density representation using monochromatic images, our prediction stage can recreate color representation on the basis of an image colorization module. We then reproduce a colorful implicit model through the representation of luminance, density, and color. Extensive experiments have been conducted to validate the effectiveness of our approaches. Our project page: https://liquidammonia.github.io/color-nerf.
Original languageEnglish
Title of host publicationProceedings of the 38th AAAI Conference on Artificial Intelligence
EditorsMichael Wooldridge, Jennifer Dy, Sriraam Natarajan
PublisherAAAI press
Pages1317-1325
Number of pages9
Volume38
Edition2
ISBN (Print)1577358872 , 9781577358879
DOIs
Publication statusPublished - 25 Mar 2024
Event38th AAAI Conference on Artificial Intelligence, AAAI 2024 - Vancouver, Canada
Duration: 20 Feb 202427 Feb 2024
https://ojs.aaai.org/index.php/AAAI/issue/archive

Publication series

NameProceedings of the AAAI Conference on Artificial Intelligence
Number2
Volume38
ISSN (Print)2159-5399
ISSN (Electronic)2374-3468

Conference

Conference38th AAAI Conference on Artificial Intelligence, AAAI 2024
Country/TerritoryCanada
CityVancouver
Period20/02/2427/02/24
Internet address

Scopus Subject Areas

  • Artificial Intelligence

User-Defined Keywords

  • Computational Photography
  • Image & Video Synthesis
  • Low Level & Physics-based Vision

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