UPST-NeRF:用于3D场景的神经辐射场的通用摄影现实风格转移
IEEE transactions on visualization and computer graphics
|March 19, 2024
概括
这项研究引入了一种新的框架,用于摄影现实 3D 场景风格化,使得在新的视图中实现一致的风格转移. 该方法克服了现有的神经辐射场技术对3D场景表示的局限性.
科学领域:
- 计算机视觉 计算机视觉
- 计算机图形 计算机图形
- 人工智能的人工智能
背景情况:
- 现有的神经辐射场用于3D场景造型的方法产生了文物,缺乏普遍适用性.
- 目前的方法需要为每一个新的风格图像重新训练,限制效率和灵活性.
研究的目的:
- 开发一个新的框架,用于摄影现实 3D 场景风格化转移.
- 为了使 2D 风格图像从 2D 风格图像进行一致的风格转移,以实现新视图视频染.
- 为了实现3D场景的通用摄影现实风格化,而无需重新训练.
主要方法:
- 预训练一个 2D 摄影现实主义风格传输网络,用于多功能图像风格化.
- 使用voxel特征优化3D场景几何,以获得场景表示.
- 共同优化一个超级网络,以在3D场景中任意转移风格.
主要成果:
- 拟议的框架成功地实现了使用2D风格图像实现光现实的3D场景风格转移.
- 与现有技术相比,该方法显示出卓越的视觉质量和一致性.
- 它可以在不同的视图中对任意的样式图像进行样式传输.
结论:
- 新的框架有效地解决了当前3D场景造型方法的局限性.
- 它提供了一个强大的解决方案,用于在3D场景中实现摄影现实性和一致的风格转移.
- 这种方法为新的视图染提供了更大的灵活性和视觉保真性.
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