视觉感知辐射场 (VPRF):用于形图像合成的视觉感知辐射场
IEEE transactions on visualization and computer graphics
|September 11, 2024
概括
我们介绍了VPRF,这是神经辐射场 (NeRF) 的一种新方法,通过结合人类视觉系统 (HVS) 模型来提高实时染质量. 这种方法优化了计算资源,以在虚拟现实应用中获得更好的视觉感知.
科学领域:
- 计算机视觉 计算机视觉
- 计算机图形 计算机图形
- 人与计算机的交互
背景情况:
- 神经辐射场 (NeRF) 在复杂3D场景的新视图合成方面表现出色.
- 当前的NeRF方法在实现实时染和虚拟现实 (VR) 的高感知质量方面面临挑战.
研究的目的:
- 提出VPRF,一种视觉感知辐射场表示方法.
- 整合人类视觉系统 (HVS) 模型,以提高VR中的染效率和感知质量.
主要方法:
- 将外观和视觉敏感性信息编码到辐射场表示中.
- 实施视觉感知采样策略,根据HVS灵敏度分配资源.
- 使用抽样重量受约束训练方案来增强辐射场表示.
主要成果:
- 与FoV-NeRF相比,VPRF在焦点和突出区域实现了更高的PSNR和SSIM的更高效染.
- 实验结果显示,关键视觉区域的性能有所改善.
结论:
- VPRF有效地将HVS模型集成到辐射场染中.
- 该方法证明了高保真度的视觉感知和VR应用程序的提高效率.
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