Estimating optical flow from successive video frames is one of the fundamental problems in computer vision and image processing. In the era of deep learning, many methods have been proposed to use convolutional neural networks (CNNs) for optical flow estimation in an unsupervised manner. However, the performance of unsupervised optical flow approaches is still unsatisfactory and often lagging far behind their supervised counterparts, primarily due to over-smoothing across motion boundaries and occlusion. To address these issues, in this paper, we propose a novel method with a new post-processing term and an effective loss function to estimate optical flow in an unsupervised, end-to-end learning manner. Specifically, we first exploit a CNN-based non-local term to refine the estimated optical flow by removing noise and decreasing blur around motion boundaries. This is implemented via automatically learning weights of dependencies over a large spatial neighborhood. Because of its learning ability, the method is effective for various complicated image sequences. Secondly, to reduce the influence of occlusion, a symmetrical energy formulation is introduced to detect the occlusion map from refined bi-directional optical flows. Then the occlusion map is integrated to the loss function. Extensive experiments are conducted on challenging datasets, i.e. FlyingChairs, MPI-Sintel and KITTI to evaluate the performance of the proposed method. The state-of-the-art results demonstrate the effectiveness of our proposed method.

中文翻译：

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基于 CNN 的非局部滤波的光流无监督学习。


从连续视频帧估计光流是计算机视觉和图像处理中的基本问题之一。在深度学习时代，人们提出了许多使用卷积神经网络（CNN）以无监督方式进行光流估计的方法。然而，无监督光流方法的性能仍然不令人满意，并且常常远远落后于有监督的同行，这主要是由于运动边界和遮挡的过度平滑。为了解决这些问题，在本文中，我们提出了一种新的方法，具有新的后处理项和有效的损失函数，以无监督的端到端学习方式估计光流。具体来说，我们首先利用基于 CNN 的非局部项，通过消除噪声和减少运动边界周围的模糊来细化估计的光流。这是通过自动学习大空间邻域的依赖权重来实现的。由于其学习能力，该方法对于各种复杂的图像序列都是有效的。其次，为了减少遮挡的影响，引入对称能量公式来从精细的双向光流中检测遮挡图。然后将遮挡图集成到损失函数中。在具有挑战性的数据集（即 FlyingChairs、MPI-Sintel 和 KITTI）上进行了大量实验，以评估所提出方法的性能。最先进的结果证明了我们提出的方法的有效性。