RecolorNeRF：面向三维场景高效颜色编辑的层分解辐射场
RecolorNeRF: Layer Decomposed Radiance Fields for Efficient Color Editing of 3D Scenes
AI总结：
RecolorNeRF通过将三维场景分解为纯色层并联合优化层与NeRF，实现高效、视图一致的调色板式颜色编辑，在复杂场景中优于基线方法。
摘要：
辐射场已逐渐成为媒体的一种主要表示形式。尽管其外观编辑已得到研究，但如何以高效的方式实现视图一致的重着色仍处于探索不足的状态。我们提出RecolorNeRF，这是一种面向神经辐射场的新型用户友好型颜色编辑方法。我们的核心思想是将场景分解为一组纯色层，从而形成一个调色板。通过这种方式，可以直接修改调色板的颜色分量来进行颜色操控。为了支持基于调色板的高效编辑，每一层的颜色需要尽可能具有代表性。最终，该问题被形式化为一个优化问题，其中各层及其混合权重与NeRF本身联合优化。大量实验表明，我们联合优化的层分解可适用于多种骨干网络，并生成照片级真实感的重着色新视角渲染结果。我们证明，即使在复杂的真实世界场景中，RecolorNeRF在颜色编辑方面也在定量和定性上优于基线方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
