Abstract

Optimizing rendering performance improves the user's immersion in virtual scene exploration. Foveated rendering uses the features of the human visual system (HVS) to improve rendering performance without sacrificing perceptual visual quality. We collect and analyze the viewing motion of different locomotion methods, and describe the effects of these viewing motions on HVS's sensitivity, as well as the advantages of these effects that may bring to foveated rendering. Then we propose the locomotion-aware foveated rendering method (LaFR) to further accelerate foveated rendering by leveraging the advantages. In LaFR, we first introduce the framework of LaFR. Secondly, we propose an eccentricity-based shading rate controller that provides the shading rate control of the given region in foveated rendering. Thirdly, we propose a locomotion-aware log-polar mapping method, which controls the foveal average shading rate, the peripheral shading rate decrease speed, and the overall shading quantity with the locomotion-aware coefficients based on the eccentricity-based shading rate controller. LaFR achieves similar perceptual visual quality as the conventional foveated rendering while achieving up to 1.6× speedup. Compared with the full resolution rendering, LaFR achieves up to 3.8× speedup.

Keywords:
Rendering (computer graphics) Computer science Computer vision Artificial intelligence Tiled rendering Speedup Real-time rendering Software rendering Shading Peripheral vision Computer graphics (images) Alternate frame rendering Perception Parallel rendering Computer graphics

Metrics

7
Cited By
1.27
FWCI (Field Weighted Citation Impact)
43
Refs
0.76
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Advanced Vision and Imaging
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Computer Graphics and Visualization Techniques
Physical Sciences →  Computer Science →  Computer Graphics and Computer-Aided Design
Visual Attention and Saliency Detection
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition

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