基于自定义焦平面引导的可变光圈散景渲染
Variable Aperture Bokeh Rendering via Customized Focal Plane Guidance
AI总结：
本文提出一种基于自定义焦平面与光圈提示的可控散景渲染方法，并构建可变光圈散景数据集 VABD，以 4.4M 参数量实现轻量且具竞争力的真实散景模拟。
摘要：
散景渲染是摄影中最受欢迎的技术之一。它能使照片更具视觉吸引力，促使用户将注意力集中在图像的特定区域。然而，获得令人满意的散景效果通常面临巨大挑战，因为移动相机受限于光学系统，而昂贵的高端大光圈单反镜头又往往不可或缺。因此，近年来许多基于深度学习的计算摄影方法被开发出来以模拟散景效果。尽管如此，这些方法大多局限于在某一固定光圈下渲染散景效果。目前缺乏用户友好、能够提供精确焦平面控制和自定义散景生成的散景渲染方法，同时也缺乏真实可信、有望促进可变光圈散景学习的散景数据集。为解决这两个问题，本文提出了一种有效的可控散景渲染方法，并贡献了可变光圈散景数据集（Variable Aperture Bokeh Dataset, VABD）。在所提方法中，用户可以自定义焦平面以准确定位关注主体，并选择目标光圈信息进行散景渲染。在公开的 EBB! 基准数据集和我们构建的 VABD 数据集上的实验结果表明，自定义焦平面与光圈提示相结合能够引导模型模拟真实的散景效果。所提方法仅以 4.4M 参数量就取得了具有竞争力的最先进性能，远轻于主流计算散景模型。所贡献的数据集和源代码将在 GitHub 上发布：https://github.com/MoTong-AI-studio/VABM。
英文摘要：
Bokeh rendering is one of the most popular techniques in photography. It can make photographs visually appealing, forcing users to focus their attentions on particular area of image. However, achieving satisfactory bokeh effect usually presents significant challenge, since mobile cameras with restricted optical systems are constrained, while expensive high-end DSLR lens with large aperture should be needed. Therefore, many deep learning-based computational photography methods have been developed to mimic the bokeh effect in recent years. Nevertheless, most of these methods were limited to rendering bokeh effect in certain single aperture. There lacks user-friendly bokeh rendering method that can provide precise focal plane control and customised bokeh generation. There as well lacks authentic realistic bokeh dataset that can potentially promote bokeh learning on variable apertures. To address these two issues, in this paper, we have proposed an effective controllable bokeh rendering method, and contributed a Variable Aperture Bokeh Dataset (VABD). In the proposed method, user can customize focal plane to accurately locate concerned subjects and select target aperture information for bokeh rendering. Experimental results on public EBB! benchmark dataset and our constructed dataset VABD have demonstrated that the customized focal plane together aperture prompt can bootstrap model to simulate realistic bokeh effect. The proposed method has achieved competitive state-of-the-art performance with only 4.4M parameters, which is much lighter than mainstream computational bokeh models. The contributed dataset and source codes will be released on github https://github.com/MoTong-AI-studio/VABM.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
