mlsanityData to 7 Oct 2026

Immersive Neural Graphics Primitives

arXiv 2211.13494 ↗First posted 24 Nov 2022cs.CV cs.GR cs.HCcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 6 of its 31 references that are in the map. First posted on arXiv 24 Nov 2022.

Builds on

6 of 31 references.

References · 6

  1. 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,698
  2. 2020Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domainscited by 2,170
  3. 2020D-NeRF: Neural Radiance Fields for Dynamic Scenescited by 1,459
  4. 2020NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collectionscited by 1,230
  5. 2022Block-NeRF: Scalable Large Scene Neural View Synthesiscited by 720
  6. 2021FoV-NeRF: Foveated Neural Radiance Fields for Virtual Realitycited by 79

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 1.

4 papers.