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 field. Our key idea is to decompose the scene into a set of pure-colored layers, forming a palette. Thus, 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 in an optimization formula, where the layers and their blending way 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代表。 虽然已经研究了它的外观编辑, 但仍在探索如何以高效的方式实现视觉一致的彩色再造。 我们展示了RecolorNeRF, 这是神经光亮场的新颖的用户友好的色彩编辑方法。 我们的主要想法是将场景分解成一组纯色层, 形成色调。 因此, 色彩调控可以通过直接改变调色板的颜色组件来进行。 支持高效的调色板编辑, 每个层的颜色都需要尽可能具有代表性。 最后, 问题将形成一种优化公式, 使层及其混合方式与 NeRF 本身共同优化。 广泛的实验显示, 我们联合优化的层分解可以用来对付多根骨, 并产生光现实化的彩色新观点。 我们证明, REcol NeRF 超越了即使在复杂的现实场景色编辑的定量和定性基准方法。