# gvnn: Neural Network Library for Geometric Computer Vision

arXiv: https://arxiv.org/abs/1607.07405 · HTML: https://mlsanity.com/paper/1607.07405/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jul 2016.

## Built on by (most cited first, 4 of 4)

- [Unsupervised Depth Completion with Calibrated Backprojection Layers](https://mlsanity.com/paper/2108.10531/) (2021)
- [Deep Learning for Camera Calibration and Beyond: A Survey](https://mlsanity.com/paper/2303.10559/) (2023)
- [Perspective Transformation Layer](https://mlsanity.com/paper/2201.05706/) (2022)
- [Validation & Exploration of Multimodal Deep-Learning Camera-Lidar Calibration models](https://mlsanity.com/paper/2409.13402/) (2024)

## Abstract (arXiv)

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.