RecolorNeRF: Layer Decomposed Radiance Fields for Efficient Color Editing of 3D Scenes
| Autor: | Gong, Bingchen, Wang, Yuehao, Han, Xiaoguang, Dou, Qi |
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| Rok vydání: | 2023 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1145/3581783.3611957 |
| Popis: | 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. Comment: To appear in ACM Multimedia 2023. Project website is accessible at https://sites.google.com/view/recolornerf |
| Databáze: | arXiv |
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