Neural radiance field (NeRF), in particular its extension by instant neural graphics primitives, is a novel rendering method for view synthesis that uses real-world images to build photo-realistic immersive virtual scenes. Despite its potential, research on the combination of NeRF and virtual reality (VR) remains sparse. Currently, there is no integration into typical VR systems available, and the performance and suitability of NeRF implementations for VR have not been evaluated, for instance, for different scene complexities or screen resolutions. In this paper, we present and evaluate a NeRF-based framework that is capable of rendering scenes in immersive VR allowing users to freely move their heads to explore complex real-world scenes. We evaluate our framework by benchmarking three different NeRF scenes concerning their rendering performance at different scene complexities and resolutions. Utilizing super-resolution, our approach can yield a frame rate of 30 frames per second with a resolution of 1280x720 pixels per eye. We discuss potential applications of our framework and provide an open source implementation online.
翻译:神经光亮场(NERF),特别是其通过瞬时神经图形原始体的扩展,是一种新的合成合成观点方法,它使用真实世界图像来建立光现实的暗地虚拟场景。尽管具有潜力,但关于NERF和虚拟现实(VR)相结合的研究仍然很少。目前,还没有与典型VR系统进行整合,也没有对NERF实施VR的性能和适宜性进行评估,例如,不同场景复杂度或屏幕分辨率。我们在本文件中介绍并评价一个基于NERF的框架,这个框架能够在隐性VR中显示场景,使用户能够自由移动头部以探索复杂的真实世界场景。我们通过对NERF的三个不同场景进行基准评估,说明它们在不同场面复杂度和分辨率上的表现。利用超分辨率,我们的方法可以产生每秒30个框架,每秒有1 280x720 像素的分辨率。我们讨论框架的潜在应用,并在网上提供开放源执行。