Colorizing Monochromatic Radiance Fields
Yean Cheng, Renjie Wan, Shuchen Weng, Chengxuan Zhu, Yakun Chang, Boxin Shi
摘要
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 .
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- GRAF: Generative Radiance Fields for 3D-Aware Image SynthesisKatja Schwarz, Yiyi Liao, Michael Niemeyer, Andreas GeigerNeurIPS 2020 · 被引用 1,001 次
- CLIP-NeRF: Text-and-Image Driven Manipulation of Neural Radiance FieldsCan Wang, Menglei Chai, Mingming He, Dongdong Chen 等CVPR 2022 · 被引用 313 次
- Editing Conditional Radiance FieldsSteven Liu, Xiuming Zhang, Zhoutong Zhang, Richard Zhang 等ICCV 2021 · 被引用 297 次
- Deblur-NeRF: Neural Radiance Fields from Blurry ImagesLi Ma, Xiaoyu Li, Jing Liao, Qi Zhang 等CVPR 2022 · 被引用 176 次
- Towards Vivid and Diverse Image Colorization with Generative Color PriorYanze Wu, Xintao Wang, Yu Li, Honglun Zhang 等ICCV 2021 · 被引用 123 次
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