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Practical Temporal Consistency for Image-Based Graphics Applications

机译:基于图像的图形应用程序的实用时间一致性

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摘要

We present an efficient and simple method for introducing temporal consistency to a large class of optimization driven image-based computer graphics problems. Our method extends recent work in edge-aware filtering, approximating costly global regularization with a fast iterative joint filtering operation. Using this representation, we can achieve tremendous efficiency gains both in terms of memory requirements and running time. This enables us to process entire shots at once, taking advantage of supporting information that exists across far away frames, something that is difficult with existing approaches due to the computational burden of video data. Our method is able to filter along motion paths using an iterative approach that simultaneously uses and estimates per-pixel optical flow vectors. We demonstrate its utility by creating temporally consistent results for a number of applications including optical flow, disparity estimation, colorization, scribble propagation, sparse data up-sampling, and visual saliency computation.
机译:我们提出了一种有效且简单的方法,用于将时间一致性引入到一类优化驱动的基于图像的计算机图形问题中。我们的方法扩展了边缘感知过滤的最新工作,并通过快速迭代联合过滤操作近似于代价高昂的全局正则化。使用这种表示形式,我们可以在内存需求和运行时间方面实现巨大的效率提升。这使我们能够利用支持在遥远帧中存在的信息来一次处理整个镜头,由于视频数据的计算负担,使用现有方法很难做到这一点。我们的方法能够使用迭代方法沿运动路径进行过滤,该迭代方法同时使用和估计每个像素的光流矢量。我们通过为许多应用程序创建时间上一致的结果来证明其效用,这些应用程序包括光流,视差估计,着色,自由曲线传播,稀疏数据上采样和视觉显着性计算。

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