交互式NeRF几何编辑与形状先验
IEEE transactions on pattern analysis and machine intelligence
|September 15, 2023
概括
这项研究引入了一种新的方法,通过提取和变形它们的内部几何来编辑神经辐射场 (NeRF). 这允许直接操纵形状,克服了以前NeRF编辑技术的局限性.
科学领域:
- 计算机视觉 计算机视觉
- 计算机图形 计算机图形
- 3D几何处理处理 3D几何处理
背景情况:
- 神经辐射场 (NeRFs) 在静态3D场景的新视图合成中表现出色.
- 修改NeRF内容是具有挑战性的,因为大型训练数据集.
- 现有的NeRF编辑方法缺乏直接形状变形的能力.
研究的目的:
- 开发一种用于直接编辑神经辐射场的几何学的方法.
- 为了实现NeRF表示的直观形状操纵.
- 将NeRF编辑扩展到高级功能,如语义部分操纵.
主要方法:
- 从NeRF的几何结构中提取一个三角形网格表示.
- 使用像ARAP变形器这样的工具将提取的网格变形.
- 将地图变形返回NeRF卷以进行修改的染.
- 引入框框抽象和语义标签,以增强编辑控制.
主要成果:
- 成功实现了NeRFs的直接形状变形.
- 通过框抽象和语义部分组合展示了直观的编辑.
- 在合成和现实世界捕获的场景上验证了该方法.
结论:
- 拟议的方法显著提高了NeRF的编辑性,特别是用于形状操纵.
- 它弥合了隐式NeRF表示和显式几何编辑之间的差距.
- 为交互式3D场景修改提供了一个强大的新工具.
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