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Our significant extension version of ACV, named Fast-ACV, will be soon available at

Method Scene Flow
KITTI 2012
KITTI 2015
Runtime (ms)
Fast-ACVNet+ 0.59 1.85 % 2.01 % 45
HITNet - 1.89 % 1.98 % 54
CoEx 0.69 1.93 % 2.13 % 33
BGNet+ - 2.03 % 2.19 % 35
AANet 0.87 2.42 % 2.55 % 62
DeepPrunerFast 0.97 - 2.59 % 50

Our Fast-ACVNet+ outperforms all the published real-time methods on Scene Flow, KITTI 2012 and KITTI 2015

ACVNet (CVPR 2022)

This is the implementation of the paper: Attention Concatenation Volume for Accurate and Efficient Stereo Matching, CVPR 2022, Gangwei Xu, Junda Cheng, Peng Guo, Xin Yang


An informative and concise cost volume representation is vital for stereo matching of high accuracy and efficiency. In this paper, we present a novel cost volume construction method which generates attention weights from correlation clues to suppress redundant information and enhance matching-related information in the concatenation volume. To generate reliable attention weights, we propose multi-level adaptive patch matching to improve the distinctiveness of the matching cost at different disparities even for textureless regions.


How to use


  • Python 3.8
  • Pytorch 1.10


Create a virtual environment and activate it.

conda create -n acvnet python=3.8
conda activate acvnet


conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch -c nvidia
pip install opencv-python
pip install scikit-image
pip install tensorboard
pip install matplotlib 
pip install tqdm

Data Preparation

Download Scene Flow Datasets, KITTI 2012, KITTI 2015


Use the following command to train ACVNet on Scene Flow

Firstly, train attention weights generation network for 64 epochs,

python --attention_weights_only True

Secondly, freeze attention weights generation network parameters, train the remaining network for another 64 epochs,

python --freeze_attention_weights True

Finally, train the complete network for 64 epochs,


Use the following command to train ACVNet on KITTI (using pretrained model on Scene Flow)




Pretrained Model

Scene Flow

Results on KITTI 2015 leaderboard

Leaderboard Link

Method D1-bg (All) D1-fg (All) D1-all (All) Runtime (s)
ACVNet 1.37 % 3.07 % 1.65 % 0.20
LEAStereo 1.40 % 2.91 % 1.65 % 0.30
GwcNet 1.74 % 3.93 % 2.11 % 0.32
PSMNet 1.86 % 4.62 % 2.32 % 0.41

Qualitative results on Scene Flow Datasets, KITTI 2012 and KITTI 2015

The left column is left image, and the right column is results of our ACVNet.



If you find this project helpful in your research, welcome to cite the paper.

  title={Attention Concatenation Volume for Accurate and Efficient Stereo Matching},
  author={Xu, Gangwei and Cheng, Junda and Guo, Peng and Yang, Xin},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},


Thanks to Xiaoyang Guo for opening source of his excellent work GwcNet. Our work is inspired by this work and part of codes are migrated from GwcNet.

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