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Volume data is found in many important scientific and engineeringapplications. Rendering this data for visualization at high quality andinteractive rates for demanding applications such as virtual reality is stillnot easily achievable even using professional-grade hardware. We introduceFoVolNet -- a method to significantly increase the performance of volume datavisualization. We develop a cost-effective foveated rendering pipeline thatsparsely samples a volume around a focal point and reconstructs the full-frameusing a deep neural network. Foveated rendering is a technique that prioritizesrendering computations around the user's focal point. This approach leveragesproperties of the human visual system, thereby saving computational resourceswhen rendering data in the periphery of the user's field of vision. Ourreconstruction network combines direct and kernel prediction methods to producefast, stable, and perceptually convincing output. With a slim design and theuse of quantization, our method outperforms state-of-the-art neuralreconstruction techniques in both end-to-end frame times and visual quality. Weconduct extensive evaluations of the system's rendering performance, inferencespeed, and perceptual properties, and we provide comparisons to competingneural image reconstruction techniques. Our test results show that FoVolNetconsistently achieves significant time saving over conventional rendering whilepreserving perceptual quality.

FoVolNet: Fast Volume Rendering using Foveated Deep Neural Networks