Radiance fields have gradually become a main representation of media. Although its appearance editing has been studied, how to achieve view-consistent recoloring in an efficient manner is still under explored. We present RecolorNeRF, a novel user-friendly color editing approach for the neural radiance fields. Our key idea is to decompose the scene into a set of pure-colored layers, forming a palette. By this means, color manipulation can be conducted by altering the color components of the palette directly. To support efficient palette-based editing, the color of each layer needs to be as representative as possible. In the end, the problem is formulated as an optimization problem, where the layers and their blending weights are jointly optimized with the NeRF itself. Extensive experiments show that our jointly-optimized layer decomposition can be used against multiple backbones and produce photo-realistic recolored novel-view renderings. We demonstrate that RecolorNeRF outperforms baseline methods both quantitatively and qualitatively for color editing even in complex real-world scenes.

翻译：辐射场已逐渐成为媒体的主要表示形式。尽管其外观编辑已得到研究，但如何以高效方式实现视图一致的重着色仍待探索。我们提出RecolorNeRF，一种新颖的用户友好的神经辐射场颜色编辑方法。核心思想是将场景分解为一系列纯色层形成调色板。通过这种方式，颜色操作可以直接通过修改调色板的颜色分量来实现。为支持基于调色板的高效编辑，每层的颜色需尽可能具有代表性。最终问题被转化为一个优化问题，其中层及其混合权重与NeRF本身联合优化。大量实验表明，我们联合优化的层分解方法可兼容多种主干网络，并生成照片级真实感的新视图重着色渲染。我们证明，即使在复杂真实场景中，RecolorNeRF在颜色编辑方面无论从定量还是定性角度均优于基线方法。