Colorizing Monochromatic Radiance Fields
Yean Cheng Renjie Wan Shuchen Weng Chengxuan Zhu Yakun Chang Boxin Shi
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.