视频Depth-from-Defocus

Hyeongwoo Kim, Christian Richardt, C. Theobalt
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引用次数: 11

摘要

如果每帧深度信息可用,许多引人注目的视频后处理效果,特别是美学焦点编辑和重新聚焦效果是可行的。现有的捕捉RGB和深度的计算方法要么有目的地修改光学(编码孔径,光场成像),要么采用主动RGB- d相机。由于这些方法对于使用普通摄像机的用户不太实用,因此我们提出了一种使用未经修改的商用摄像机捕获动态场景全焦RGB-D视频的算法。我们的算法将通常不需要的散焦模糊转化为有价值的信号。我们的方法的输入是一个视频,在这个视频中,焦平面在捕捉过程中不断地前后移动,因此引起了离焦模糊,并且非常明显。这可以通过在记录过程中手动转动镜头的对焦环来实现。算法的核心部分是一种新的基于视频的离焦深度算法,该算法计算时空相干深度图,去模糊全焦视频,以及每帧的焦距。我们广泛评估了我们的方法,并表明它可以实现引人注目的视频后处理效果,例如不同类型的重新对焦。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Video Depth-from-Defocus
Many compelling video post-processing effects, in particular aesthetic focus editing and refocusing effects, are feasible if per-frame depth information is available. Existing computational methods to capture RGB and depth either purposefully modify the optics (coded aperture, light-field imaging), or employ active RGB-D cameras. Since these methods are less practical for users with normal cameras, we present an algorithm to capture all-in-focus RGB-D video of dynamic scenes with an unmodified commodity video camera. Our algorithm turns the often unwanted defocus blur into a valuable signal. The input to our method is a video in which the focus plane is continuously moving back and forth during capture, and thus defocus blur is provoked and strongly visible. This can be achieved by manually turning the focus ring of the lens during recording. The core algorithmic ingredient is a new video-based depth-from-defocus algorithm that computes space-time-coherent depth maps, deblurred all-in-focus video, and the focus distance for each frame. We extensively evaluate our approach, and show that it enables compelling video post-processing effects, such as different types of refocusing.
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