The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
DeepPruner: Learning Efficient Stereo Matching via Differentiable PatchMatch
Open accessAbstract
Our goal is to significantly speed up the runtime of current state-of-the-art stereo algorithms to enable real-time inference. Towards this goal, we developed a differentiable PatchMatch module that allows us to discard most disparities without requiring full cost volume evaluation. We then exploit this representation to learn which range to prune for each pixel. By progressively reducing the search space and effectively propagating such information, we are able to efficiently compute the cost volume for high likelihood hypotheses and achieve savings in both memory and computation.Finally, an image guided refinement module is exploited to further improve the performance. Since all our components are differentiable, the full network can be trained end-to-end. Our experiments show that our method achieves competitive results on KITTI and SceneFlow datasets while running in real-time at 62ms.
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Cites
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Self-citationA Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
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Stereo Processing by Semiglobal Matching and Mutual Information
Hirschmuller, H. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1166 · Cited by 4,195
BRIEF: Binary Robust Independent Elementary Features
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Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
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PatchMatch: a randomized correspondence algorithm for structural image editing
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Cited by
PatchmatchNet: Learned Multi-View Patchmatch Stereo
Wang, F., Galliani, S., Vogel, C., Speciale, P., & Pollefeys, M. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01397 · Cited by 420
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
HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matching
Tankovich, V., Hane, C., Zhang, Y., Kowdle, A., Fanello, S., & Bouaziz, S. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01413 · Cited by 309
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
- Duggal, S., Wang, S., Ma, W.-C., Hu, R., & Urtasun, R.
- Venue
- 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2019-10
- Pages
- 4383-4392
- OpenAlex
- W2972703007
- Cited by
- 317
- References
- 38
- FWCI
- 12.90
- Type
- conference paper
- Language
- en
- Open access
- green
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- Duggal, S., Wang, S., Ma, W.-C., Hu, R., & Urtasun, R. (2019). DeepPruner: Learning Efficient Stereo Matching via Differentiable PatchMatch. 2019 IEEE/CVF International Conference on Computer Vision (ICCV). https://doi.org/10.1109/iccv.2019.00448
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