A3FR: Agile 3D Gaussian Splatting with Incremental Gaze Tracked Foveated Rendering in Virtual Reality
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866916828950822912 |
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| author | Xin, Shuo Wang, Haiyu Zhang, Sai Qian |
| author_facet | Xin, Shuo Wang, Haiyu Zhang, Sai Qian |
| contents | Virtual reality (VR) significantly transforms immersive digital interfaces, greatly enhancing education, professional practices, and entertainment by increasing user engagement and opening up new possibilities in various industries. Among its numerous applications, image rendering is crucial. Nevertheless, rendering methodologies like 3D Gaussian Splatting impose high computational demands, driven predominantly by user expectations for superior visual quality. This results in notable processing delays for real-time image rendering, which greatly affects the user experience. Additionally, VR devices such as head-mounted displays (HMDs) are intricately linked to human visual behavior, leveraging knowledge from perception and cognition to improve user experience. These insights have spurred the development of foveated rendering, a technique that dynamically adjusts rendering resolution based on the user's gaze direction. The resultant solution, known as gaze-tracked foveated rendering, significantly reduces the computational burden of the rendering process.
Although gaze-tracked foveated rendering can reduce rendering costs, the computational overhead of the gaze tracking process itself can sometimes outweigh the rendering savings, leading to increased processing latency. To address this issue, we propose an efficient rendering framework called~\textit{A3FR}, designed to minimize the latency of gaze-tracked foveated rendering via the parallelization of gaze tracking and foveated rendering processes. For the rendering algorithm, we utilize 3D Gaussian Splatting, a state-of-the-art neural rendering technique. Evaluation results demonstrate that A3FR can reduce end-to-end rendering latency by up to $2\times$ while maintaining visual quality. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_04147 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A3FR: Agile 3D Gaussian Splatting with Incremental Gaze Tracked Foveated Rendering in Virtual Reality Xin, Shuo Wang, Haiyu Zhang, Sai Qian Graphics Computer Vision and Pattern Recognition Distributed, Parallel, and Cluster Computing Virtual reality (VR) significantly transforms immersive digital interfaces, greatly enhancing education, professional practices, and entertainment by increasing user engagement and opening up new possibilities in various industries. Among its numerous applications, image rendering is crucial. Nevertheless, rendering methodologies like 3D Gaussian Splatting impose high computational demands, driven predominantly by user expectations for superior visual quality. This results in notable processing delays for real-time image rendering, which greatly affects the user experience. Additionally, VR devices such as head-mounted displays (HMDs) are intricately linked to human visual behavior, leveraging knowledge from perception and cognition to improve user experience. These insights have spurred the development of foveated rendering, a technique that dynamically adjusts rendering resolution based on the user's gaze direction. The resultant solution, known as gaze-tracked foveated rendering, significantly reduces the computational burden of the rendering process. Although gaze-tracked foveated rendering can reduce rendering costs, the computational overhead of the gaze tracking process itself can sometimes outweigh the rendering savings, leading to increased processing latency. To address this issue, we propose an efficient rendering framework called~\textit{A3FR}, designed to minimize the latency of gaze-tracked foveated rendering via the parallelization of gaze tracking and foveated rendering processes. For the rendering algorithm, we utilize 3D Gaussian Splatting, a state-of-the-art neural rendering technique. Evaluation results demonstrate that A3FR can reduce end-to-end rendering latency by up to $2\times$ while maintaining visual quality. |
| title | A3FR: Agile 3D Gaussian Splatting with Incremental Gaze Tracked Foveated Rendering in Virtual Reality |
| topic | Graphics Computer Vision and Pattern Recognition Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2507.04147 |