We introduce gvnn, a neural network library in Torch aimed towards bridging the gap between classic geometric computer vision and deep learning. Inspired by the recent success of Spatial Transformer Networks, we propose several new layers which are often used as parametric transformations on the data in geometric computer vision. These layers can be inserted within a neural network much in the spirit of the original spatial transformers and allow backpropagation to enable end-to-end learning of a network involving any domain knowledge in geometric computer vision. This opens up applications in learning invariance to 3D geometric transformation for place recognition, end-to-end visual odometry, depth estimation and unsupervised learning through warping with a parametric transformation for image reconstruction error.

该论文介绍了gvnn，一个神经网络库，旨在弥合传统几何计算机视觉和深度学习之间的差距。作者提出了一些新的层，可以插入神经网络，用于几何计算机视觉中的参数化变换，实现端到端的学习，实现对三维几何变换的不变性学习，并通过参数化变换实现无监督学习，用于图像重建误差。

gvnn：几何计算机视觉的神经网络库