论文标题
对移动GPU，移动AI和AIM 2022挑战的现实散景效果渲染：报告
Realistic Bokeh Effect Rendering on Mobile GPUs, Mobile AI & AIM 2022 challenge: Report
论文作者
论文摘要
由于带有紧凑型光学的移动摄像机无法产生强大的散景效果，因此现在有很多兴趣致力于为此任务提供基于学习的解决方案。在此移动AI挑战中，目标是开发一种有效的端到端AI玻璃效果渲染方法，该方法可以使用Tensorflow Lite在现代智能手机GPU上运行。为参与者提供了大规模的退潮！散景数据集由使用佳能7D DSLR摄像机捕获的5K浅 /宽大图像对组成。在Kirin 9000的Mali GPU上评估了所得模型的运行时间，该运行时间为大多数常见的深度学习OPS提供了出色的加速结果。本文提供了本挑战中所有模型的详细描述。
As mobile cameras with compact optics are unable to produce a strong bokeh effect, lots of interest is now devoted to deep learning-based solutions for this task. In this Mobile AI challenge, the target was to develop an efficient end-to-end AI-based bokeh effect rendering approach that can run on modern smartphone GPUs using TensorFlow Lite. The participants were provided with a large-scale EBB! bokeh dataset consisting of 5K shallow / wide depth-of-field image pairs captured using the Canon 7D DSLR camera. The runtime of the resulting models was evaluated on the Kirin 9000's Mali GPU that provides excellent acceleration results for the majority of common deep learning ops. A detailed description of all models developed in this challenge is provided in this paper.
