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 language | English |
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Title of host publication | Proceedings of the 38th AAAI Conference on Artificial Intelligence |
Editors | Michael Wooldridge, Jennifer Dy, Sriraam Natarajan |
Publisher | AAAI press |
Pages | 1317-1325 |
Number of pages | 9 |
Volume | 38 |
Edition | 2 |
ISBN (Print) | 1577358872 , 9781577358879 |
DOIs | |
Publication status | Published - 25 Mar 2024 |
Event | 38th AAAI Conference on Artificial Intelligence, AAAI 2024 - Vancouver, Canada Duration: 20 Feb 2024 → 27 Feb 2024 https://ojs.aaai.org/index.php/AAAI/issue/archive (Conference proceeding) |
Publication series
Name | Proceedings of the AAAI Conference on Artificial Intelligence |
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Number | 2 |
Volume | 38 |
ISSN (Print) | 2159-5399 |
ISSN (Electronic) | 2374-3468 |
Conference
Conference | 38th AAAI Conference on Artificial Intelligence, AAAI 2024 |
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Country/Territory | Canada |
City | Vancouver |
Period | 20/02/24 → 27/02/24 |
Internet address |
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Scopus Subject Areas
- Artificial Intelligence
User-Defined Keywords
- Computational Photography
- Image & Video Synthesis
- Low Level & Physics-based Vision