论文标题
Oflib：促进Python中的光流场和上的操作
Oflib: Facilitating Operations with and on Optical Flow Fields in Python
论文作者
论文摘要
我们为光流的表征和操纵（即2D向量场）提出了一个强大的理论框架，即在运动估计算法及其他方面的使用。参考指南的两个帧的定义是流场应用，反转，评估和组成操作的数学推导。然后，这种结构化方法被用作Python 3中实现的基础，完全可区分的pytorch版本的libpytorch支持了深度学习所需的后传播。我们从经验上验证流量组成方法，并为其在合成训练数据创建中的光流地面真相提供了一个工作示例。所有代码均可公开使用。
We present a robust theoretical framework for the characterisation and manipulation of optical flow, i.e 2D vector fields, in the context of their use in motion estimation algorithms and beyond. The definition of two frames of reference guides the mathematical derivation of flow field application, inversion, evaluation, and composition operations. This structured approach is then used as the foundation for an implementation in Python 3, with the fully differentiable PyTorch version oflibpytorch supporting back-propagation as required for deep learning. We verify the flow composition method empirically and provide a working example for its application to optical flow ground truth in synthetic training data creation. All code is publicly available.
