Whether to attract viewer attention to a particular object, give the impression of depth or simply reproduce human-like scene perception, shallow depth of field images are used extensively by professional and amateur photographers alike. To this end, high quality optical systems are used in DSLR cameras to focus on a specific depth plane while producing visually pleasing bokeh. We propose a physically motivated pipeline to mimic this effect from all-in-focus stereo images, typically retrieved by mobile cameras. It is capable to change the focal plane a posteriori at 76 FPS on KITTI images to enable real-time applications. As our portmanteau suggests, SteReFo interrelates stereo-based depth estimation and refocusing efficiently. In contrast to other approaches, our pipeline is simultaneously fully differentiable, physically motivated, and agnostic to scene content. It also enables computational video focus tracking for moving objects in addition to refocusing of static images. We evaluate our approach on the publicly available datasets SceneFlow, KITTI, CityScapes and quantify the quality of architectural changes.

本文提出了一个基于双目图像深度估计和重聚的物理学启示管道，以在移动相机中从全聚焦双目图像中模仿DSLR相机的浅景深效果，从而实现在76 FPS的实时应用。相对于其他方法，我们的管道同时是可完全区分的、受物理启发的、不受场景内容的影响，还可以在静态图像场景中实现计算机视频焦点跟踪，我们还使用SceneFlow、KITTI、CityScapes等公共数据集对我们的方法进行了评估。

SteReFo: 基于立体视觉的高效图像重新聚焦