现场意识的状神经辐射场
IEEE transactions on visualization and computer graphics
|July 16, 2024
概括
我们介绍了一种场景感知型形神经辐射场 (NeRF) 方法,用于在复杂的VR场景中高质量的形图像合成. 与现有方法相比,这种方法显著提高了性能和视觉质量.
科学领域:
- 计算机视觉 计算机视觉
- 计算机图形 计算机图形
- 虚拟现实 虚拟现实 虚拟现实
背景情况:
- 神经辐射场 (NeRF) 方法为图像合成提供了潜力.
- 形染旨在通过专注于突出区域来提高性能.
- 现有的NeRF方法在复杂的VR场景中扎着高质量的形图像合成.
研究的目的:
- 为复杂的VR场景中提供高质量的foveated图像合成提供场景感应的foveated神经辐射场方法.
- 增强NeRF在复杂VR场景的突出区域的表现能力.
- 为了实现高率的foveated图像合成.
主要方法:
- 基于场景内容构建一个多圆形的神经表征.
- 实施基于统一采样的状神经辐射场框架.
- 采用面向的场景感知目标功能,以提高合成质量.
主要成果:
- 在复杂场景中以平均66 FPS合成高质量的双筒形图像.
- 在和外围区域实现了显著更高的合成质量.
- 与最先进的 foveated NeRF 方法相比,证明了 1.41-1.46 倍的加快速度.
- 用户研究证实了高视觉相似性与地面真相.
结论:
- 拟议的场景感知形NeRF方法有效地在复杂的VR环境中合成高质量的形图像.
- 该方法在合成质量和染速度方面取得了实质性的改进.
- 该方法显示了对需要高视觉保真度的实时VR应用程序的巨大潜力.
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