Immersive Neural Graphics Primitives
This work addresses the problem of enabling immersive VR exploration of complex real-world scenes for users, but it is incremental as it builds on existing NeRF methods.
The paper tackles the lack of integration and evaluation of neural radiance fields (NeRF) in virtual reality (VR) systems, presenting a framework that achieves 30 frames per second at 1280x720 pixels per eye using super-resolution.
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.