The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Attention Concatenation Volume for Accurate and Efficient Stereo Matching
Open accessAbstract
Stereo matching is a fundamental building block for many vision and robotics applications. 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. The proposed cost volume is named attention concatenation volume (ACV) which can be seamlessly embedded into most stereo matching networks, the resulting networks can use a more lightweight aggregation network and meanwhile achieve higher accuracy, e.g. using only 1/25 parameters of the aggregation network can achieve higher accuracy for GwcNet. Furthermore, we design a highly accurate network (ACVNet) based on our ACV, which achieves state-of-the-art performance on several benchmarks. The code is available at https://github.com/gangweiX/ACVNet.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Are we ready for autonomous driving? The KITTI vision benchmark suite
Geiger, A., Lenz, P., & Urtasun, R. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6248074 · Cited by 14,897
Mayer, N., Ilg, E., Hausser, P., Fischer, P., Cremers, D., Dosovitskiy, A., & Brox, T. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.438 · Cited by 2,488
Pyramid Stereo Matching Network
Chang, J.-R., & Chen, Y.-S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00567 · Cited by 1,807
End-to-End Learning of Geometry and Context for Deep Stereo Regression
Kendall, A., Martirosyan, H., Dasgupta, S., Henry, P., Kennedy, R., Bachrach, A., & Bry, A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.17 · Cited by 1,470
A Multi-view Stereo Benchmark with High-Resolution Images and Multi-camera Videos
Schops, T., Schonberger, J. L., Galliani, S., Sattler, T., Schindler, K., Pollefeys, M., & Geiger, A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.272 · Cited by 915
Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo Matching
Gu, X., Fan, Z., Zhu, S., Dai, Z., Tan, F., & Tan, P. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00257 · Cited by 864
GA-Net: Guided Aggregation Net for End-To-End Stereo Matching
Zhang, F., Prisacariu, V., Yang, R., & Torr, P. H. S. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00027 · Cited by 811
Computing the stereo matching cost with a convolutional neural network
Zbontar, J., & LeCun, Y. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298767 · Cited by 796
Cited by
CroCo v2: Improved Cross-view Completion Pre-training for Stereo Matching and Optical Flow
Weinzaepfel, P., Lucas, T., Leroy, V., Cabon, Y., Arora, V., Brégier, R., Csurka, G., Antsfeld, L., Chidlovskii, B., & Revaud, J. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01647 · Cited by 110
Metadata
- Authors
- Xu, G., Cheng, J., Guo, P., & Yang, X.
- Venue
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2022-06
- Pages
- 12971-12980
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4312388571
- Cited by
- 291
- References
- 24
- FWCI
- 24.92
- Type
- conference paper
- Language
- en
- Open access
- green
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- Xu, G., Cheng, J., Guo, P., & Yang, X. (2022). Attention Concatenation Volume for Accurate and Efficient Stereo Matching. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr52688.2022.01264
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