计算机科学
渲染(计算机图形)
人工智能
体绘制
计算机视觉
可视化
平铺渲染
帧速率
图形管道
软件渲染
三维渲染
计算机图形学(图像)
交替帧渲染
人工神经网络
基于图像的建模与绘制
实时渲染
虚拟现实
计算机图形学
三维计算机图形学
作者
David T. Bauer,Qi Wu,Kwan‐Liu Ma
出处
期刊:IEEE Transactions on Visualization and Computer Graphics
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:29 (1): 515-525
被引量:3
标识
DOI:10.1109/tvcg.2022.3209498
摘要
Volume data is found in many important scientific and engineering applications. Rendering this data for visualization at high quality and interactive rates for demanding applications such as virtual reality is still not easily achievable even using professional-grade hardware. We introduce FoVolNet—a method to significantly increase the performance of volume data visualization. We develop a cost-effective foveated rendering pipeline that sparsely samples a volume around a focal point and reconstructs the full-frame using a deep neural network. Foveated rendering is a technique that prioritizes rendering computations around the user's focal point. This approach leverages properties of the human visual system, thereby saving computational resources when rendering data in the periphery of the user's field of vision. Our reconstruction network combines direct and kernel prediction methods to produce fast, stable, and perceptually convincing output. With a slim design and the use of quantization, our method outperforms state-of-the-art neural reconstruction techniques in both end-to-end frame times and visual quality. We conduct extensive evaluations of the system's rendering performance, inference speed, and perceptual properties, and we provide comparisons to competing neural image reconstruction techniques. Our test results show that FoVolNet consistently achieves significant time saving over conventional rendering while preserving perceptual quality.
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