Neural radiance field (NeRF) based methods enable high-quality novel-view synthesis for multi-view images. This work presents a method for synthesizing colorized novel views from input grey-scale multi-view images. When we apply image or video-based colorization methods on the generated grey-scale novel views, we observe artifacts due to inconsistency across views. Training a radiance field network on the colorized grey-scale image sequence also does not solve the 3D consistency issue. We propose a distillation based method to transfer color knowledge from the colorization networks trained on natural images to the radiance field network. Specifically, our method uses the radiance field network as a 3D representation and transfers knowledge from existing 2D colorization methods. The experimental results demonstrate that the proposed method produces superior colorized novel views for indoor and outdoor scenes while maintaining cross-view consistency than baselines. Further, we show the efficacy of our method on applications like colorization of radiance field network trained from 1.) Infra-Red (IR) multi-view images and 2.) Old grey-scale multi-view image sequences.

翻译：神经辐射场（NeRF）方法能够对多视角图像实现高质量的新视角合成。本文提出一种从输入灰度多视角图像合成彩色新视角的方法。当我们将基于图像或视频的着色方法应用于生成的灰度新视角时，由于视角间不一致性会出现伪影。在着色后的灰度图像序列上训练辐射场网络也无法解决三维一致性问题。我们提出一种基于蒸馏的方法，将自然图像上训练的着色网络的色彩知识迁移至辐射场网络。具体而言，该方法利用辐射场网络作为三维表征，并从现有二维着色方法中迁移知识。实验结果表明，相较于基线方法，所提方法能够在保持跨视角一致性的同时，为室内外场景生成更优的彩色新视角。此外，我们展示了该方法在以下两种场景中的有效性：1）红外多视角图像训练所得辐射场网络的着色；2）老式灰度多视角图像序列训练所得辐射场网络的着色。