# Foveated Rendering: Motivation, Taxonomy, and Research Directions

arXiv: https://arxiv.org/abs/2205.04529 · HTML: https://mlsanity.com/paper/2205.04529/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2022.

## Built on by (most cited first, 4 of 4)

- [VR-Splatting: Foveated Radiance Field Rendering via 3D Gaussian Splatting and Neural Points](https://mlsanity.com/paper/2410.17932/) (2024)
- [Human Eyes Inspired Recurrent Neural Networks are More Robust Against Adversarial Noises](https://mlsanity.com/paper/2206.07282/) (2022)
- [BOXR: Body and head motion Optimization framework for eXtended Reality](https://mlsanity.com/paper/2410.13084/) (2024)
- [Telescope: Learnable Hyperbolic Foveation for Ultra-Long-Range Object Detection](https://mlsanity.com/paper/2604.06332/) (2026)

## Abstract (arXiv)

With the recent interest in virtual reality and augmented reality, there is a newfound demand for displays that can provide high resolution with a wide field of view (FOV). However, such displays incur significantly higher costs for rendering the larger number of pixels. This poses the challenge of rendering realistic real-time images that have a wide FOV and high resolution using limited computing resources. The human visual system does not need every pixel to be rendered at a uniformly high quality. Foveated rendering methods provide perceptually high-quality images while reducing computational workload and are becoming a crucial component for large-scale rendering. In this paper, we present key motivations, research directions, and challenges for leveraging the limitations of the human visual system as they relate to foveated rendering. We provide a taxonomy to compare and contrast various foveated techniques based on key factors. We also review aliasing artifacts arising due to foveation methods and discuss several approaches that attempt to mitigate such effects. Finally, we present several open problems and possible future research directions that can further reduce computational costs while generating perceptually high-quality renderings.