ACNe: Attentive Context Normalization for Robust Permutation-Equivariant Learning (CVPR2020)

Introduction

This repository is the Tensorflow implementation for "ACNe: Attentive Context Normalization for Robust Permutation-Equivariant Learning" by Weiwei Sun, Wei Jiang, Eduard Trulls, Andrea Tagliasacchi, Kwang Moo Yi. If you use this code in your research, please cite the paper. In addition, if you are using the part of the code related to CNe, please also cite the CNe paper.

[Paper]   [code]

Citations

    @inproceedings{sun2020acne,
      title={Attentive Context Normalization for Robust Permutation-Equivariant Learning},
      author={Weiwei Sun, Wei Jiang, Andrea Tagliasacchi, Eduard Trulls, Kwang Moo Yi},
      booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
      year={2020},
    }
    @inproceedings{yi2018learning,
      title={Learning to Find Good Correspondences},
      author={Kwang Moo Yi* and Eduard Trulls* and Yuki Ono and Vincent Lepetit and Mathieu Salzmann and Pascal Fua},
      booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
      year={2018}
    }

Abstraction

Many problems in computer vision require dealing with sparse, unordered data in the form of point clouds. Permutation-equivariant networks have become a popular solution-they operate on individual data points with simple perceptrons and extract contextual information with global pooling. This can be achieved with a simple normalization of the feature maps, a global operation that is unaffected by the order. In this paper, we propose Attentive Context Normalization (ACN), a simple yet effective technique to build permutation-equivariant networks robust to outliers. Specifically, we show how to normalize the feature maps with weights that are estimated within the network, excluding outliers from this normalization. We use this mechanism to leverage two types of attention: local and global-by combining them, our method is able to find the essential data points in high-dimensional space to solve a given task. We demonstrate through extensive experiments that our approach, which we call Attentive Context Networks (ACNe), provides a significant leap in performance compared to the state-of-the-art on camera pose estimation, robust fitting, and point cloud classification under noise and outliers.

Requirements

Please use Tensorflow (1.8 is recommended). Please also install other dependencies via pip

Usage

Data Preparation

We used the dataset from CNe and OANet. Note that CNe dataset has a very small size. Users can quickly run the code with this dataset.

Dataset from CNe

We use the following script to generate data for st_peters_square. It will create a folder data_dump/st_peters_square.

mkdir datasets
cd datasets
wget http://webhome.cs.uvic.ca/~kyi/files/2018/learned-correspondence/st_peters_square.tar.gz
tar -xvf st_peters_square.tar.gz
rm st_peters_square.tar.gz
cd ..
./dump_data.py

Dataset from OANet

We use OANet's official repo to generate the dataset. It will create a folder data_dump. Please link the data folder as data_dump_oan.

Training:

Fundamental Matrix Estimation

bash jobs/acne.sh train

Essential Matrix Estimation

bash jobs/acne_E.sh train

For easy comparison, scripts for CNe (i.e., jobs/CNe.sh) are also provided.

Testing:

We provided the pretrained model here.

Fundamental Matrix Estimation

bash jobs/acne.sh

Essential Matrix Estimation

bash jobs/acne_E.sh

Please note that, in the testing phase, we use the bidirectional check by setting --prefiltering=B. It can be disabled by removing this argument.

Acknowledgement

The code is heavily based on CNe.

The code for fundamental matrix estimation is partly borrowed from OANet