The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
GA-Net: Guided Aggregation Net for End-To-End Stereo Matching
Open accessAbstract
In the stereo matching task, matching cost aggregation is crucial in both traditional methods and deep neural network models in order to accurately estimate disparities. We propose two novel neural net layers, aimed at capturing local and the whole-image cost dependencies respectively. The first is a semi-global aggregation layer which is a differentiable approximation of the semi-global matching, the second is the local guided aggregation layer which follows a traditional cost filtering strategy to refine thin structures. These two layers can be used to replace the widely used 3D convolutional layer which is computationally costly and memory-consuming as it has cubic computational/memory complexity. In the experiments, we show that nets with a two-layer guided aggregation block easily outperform the state-of-the-art GC-Net which has nineteen 3D convolutional layers. We also train a deep guided aggregation network (GA-Net) which gets better accuracies than state-of-the-art methods on both Scene Flow dataset and KITTI benchmarks.
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Cites
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
Bilateral Filtering for Gray and Color Images
Tomasi, C., & Manduchi, R. (2002) · Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271) · doi:10.1109/iccv.1998.710815 · Cited by 8,177 · cited 2×
“An efficient cost aggregation method is the local cost filter framework [10, 31], where each slice of the cost volume C(d) is filtered independently by a guided image filter [8, 25, 31].”
MethodStacked Hourglass Networks for Human Pose Estimation
Newell, A., Yang, K., & Deng, J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46484-8_29 · Cited by 5,634
He, K., Sun, J., & Tang, X. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.213 · Cited by 5,546
Striving for Simplicity: The All Convolutional Net
Springenberg, J. T., Dosovitskiy, A., Brox, T., & Riedmiller, M. (2014) · arXiv · doi:10.48550/arxiv.1412.6806 · Cited by 3,447
Object scene flow for autonomous vehicles
Menze, M., & Geiger, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298925 · Cited by 2,270
Cited by
Image Matching from Handcrafted to Deep Features: A Survey
Ma, J., Jiang, X., Fan, A., Jiang, J., & Yan, J. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-020-01359-2 · Cited by 990
Artificial intelligence in the creative industries: a review
Anantrasirichai, N., & Bull, D. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-021-10039-7 · Cited by 793
A Survey on Deep Learning Techniques for Stereo-Based Depth Estimation
Laga, H., Jospin, L. V., Boussaid, F., & Bennamoun, M. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3032602 · Cited by 336
Revisiting Stereo Depth Estimation From a Sequence-to-Sequence Perspective with Transformers
Li, Z., Liu, X., Drenkow, N., Ding, A., Creighton, F. X., Taylor, R. H., & Unberath, M. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00614 · Cited by 335
Attention Concatenation Volume for Accurate and Efficient Stereo Matching
Xu, G., Cheng, J., Guo, P., & Yang, X. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01264 · Cited by 291
Metadata
- Authors
- Zhang, F., Prisacariu, V., Yang, R., & Torr, P. H. S.
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 185-194
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2952813711
- Cited by
- 811
- References
- 6
- FWCI
- 31.12
- Type
- conference paper
- Language
- en
- Open access
- green
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- Zhang, F., Prisacariu, V., Yang, R., & Torr, P. H. S. (2019). GA-Net: Guided Aggregation Net for End-To-End Stereo Matching. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2019.00027
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