V4D:Voxel为4D新型视图合成提供了4D视图
IEEE transactions on visualization and computer graphics
|September 5, 2023
概括
本研究介绍了V4D,这是一种新的4D神经辐射场方法,使用3Dvoxel进行动态场景新视图合成. 它以提高效率和较低的计算成本实现了最先进的结果.
科学领域:
- 计算机视觉 计算机视觉
- 3D 图形 3D 图形
- 机器学习 机器学习
背景情况:
- 神经辐射场在静态3D场景中表现出色,但由于神经网络的局限性,在动态4D场景中扎.
- 4D神经辐射场的现有方法经常面临性能限制.
研究的目的:
- 提出一种高效和有效的方法,用于在动态4D场景中新的视图合成.
- 为了提高动态环境的神经辐射场的性能.
主要方法:
- 使用3D语音模拟4D神经辐射场 (V4D).
- 采用两个voxel格式:常规的3D空间建模和查看表 (LUTs) 以实现像素级精细化.
- 引入一个针对4D数据优化的条件定位编码方法.
主要成果:
- 基于LUT的精细化模块显著提高了性能,最小的计算开销.
- 拟议的条件定位编码增强了可以忽略不计的计算负担的结果.
- 在4D新视图合成中,V4D以较低的计算成本实现了最先进的性能.
结论:
- V4D为动态场景新视图合成提供了一种高效的解决方案.
- 基于LUT的改进模块是现有系统的多功能,插即用组件.
- 该方法与以前的方法相比,显示出更高的性能和效率.
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