论文标题
关于Kornia的调查：Pytorch的开源可区分计算机视觉库
A survey on Kornia: an Open Source Differentiable Computer Vision Library for PyTorch
论文作者
论文摘要
这项工作提出了Kornia，这是一个开源计算机视觉库，建立在旨在解决通用计算机视觉问题的一组可区分的例程和模块上。该软件包使用Pytorch作为其主要后端，不仅为了效率，而且还要利用反向自动差异引擎来定义和计算复杂功能的梯度。 Inspired by OpenCV, Kornia is composed of a set of modules containing operators that can be integrated into neural networks to train models to perform a wide range of operations including image transformations,camera calibration, epipolar geometry, and low level image processing techniques, such as filtering and edge detection that operate directly on high dimensional tensor representations on graphical processing units, generating faster systems.提供了使用我们的框架实现的经典视觉问题的示例，包括与现有视觉库进行比较的基准。
This work presents Kornia, an open source computer vision library built upon a set of differentiable routines and modules that aims to solve generic computer vision problems. The package uses PyTorch as its main backend, not only for efficiency but also to take advantage of the reverse auto-differentiation engine to define and compute the gradient of complex functions. Inspired by OpenCV, Kornia is composed of a set of modules containing operators that can be integrated into neural networks to train models to perform a wide range of operations including image transformations,camera calibration, epipolar geometry, and low level image processing techniques, such as filtering and edge detection that operate directly on high dimensional tensor representations on graphical processing units, generating faster systems. Examples of classical vision problems implemented using our framework are provided including a benchmark comparing to existing vision libraries.
