Colorizing Monochromatic Radiance Fields
This addresses the need for color in 3D scene reconstruction from monochromatic data, which is an incremental improvement for computer vision and graphics applications.
The paper tackles the problem of generating colorful 3D representations from monochromatic images using Neural Radiance Fields (NeRF), achieving this by treating it as a representation-prediction task in the Lab color space with an image colorization module.
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.