Optical flow estimation is an important hot research in computer vision. Although existing methods had got a considerable progress in improving their performance, they still have drawbacks, such as heavily computational burden, inaccurate pixel-level offset estimation, and poor interpretability. To address these issues, this letter proposes a pyramidal deep Taylor expansion (PDTE) framework, including: First, we seriously interpreted the relationship between optical flow computation and Taylor expansion. Then, the proposed PDTE is constructed by employing global motion aggregation (GMA) to calculate each derivative part which contributes to the final estimated optical flow. Quantitative and qualitative results on the and datasets validate that the proposed PDTE scheme is effective and outperforms the state-of-the-art optical flow estimation methods. The results of extensive experiments in the ablation study demonstrate that PDTE performs well on shape preservation and the accuracy improvement of optical flow estimation, even pixel-level offset calculation.

中文翻译：

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PDTE：用于光流估计的金字塔深泰勒展开

光流估计是计算机视觉领域的一个重要研究热点。尽管现有方法在提高性能方面取得了相当大的进步，但它们仍然存在缺点，例如计算负担重、像素级偏移估计不准确和可解释性差。为了解决这些问题，这封信提出了金字塔深度泰勒展开（PDTE）框架，包括：首先，我们认真解读了光流计算和泰勒展开之间的关系。然后，通过采用全局运动聚合（GMA）来计算有助于最终估计光流的每个导数部分来构建所提出的PDTE。和数据集的定量和定性结果验证了所提出的 PDTE 方案是有效的并且优于最先进的光流估计方法。烧蚀研究中的大量实验结果表明，PDTE 在形状保持和光流估计甚至像素级偏移计算的精度提高方面表现良好。