Colorizing Monochromatic Radiance Fields

AAAI 2024 (Oral presentation)

Yean Cheng1,2,3, Renjie Wan4, Shuchen Weng1,2, Chengxuan Zhu5, Yakun Chang1,2, Boxin Shi1,2,3
1 Natl. Engineering Research Center of Visual Technology, School of Computer Science, Peking University 2 Natl. Key Lab. for Multimedia Information Processing, School of Computer Science, Peking University 3 AI Innovation Center, School of Computer Science, Peking University 4 Department of Computer Science, Hong Kong Baptist University 5 National Key Lab of General AI, School of Intelligence Science and Technology, Peking University

Color-NeRF colorizes monochromatic NeRFs with both plausibility (i.e., cross-view consistency) and vividness.

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.

Detailed Comparison

BibTeX

@inproceedings{cheng2024colornerf,
  author    = {Yean Cheng, Renjie Wan, Shuchen Weng, Chengxuan Zhu, Yakun Chang, Boxin Shi},
  title     = {Colorizing Monochromatic Radiance Fields},
  journal   = {AAAI},
  year      = {2024},
}