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We present LightStereo, a cutting-edge stereo-matching network crafted to accelerate the matching process.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “ImageNet: A large-scale hierarchical image database,” in CVPR , 2009
2009
Earlier work this paper cites.
A. Geiger, P. Lenz, and R. Urtasun, “Are we ready for autonomous driving? the kitti vision benchmark suite,” in CVPR , 2012
2012
Earlier work this paper cites.
D. Scharstein, H. Hirschmüller, Y. Kitajima, G. Krathwohl, N. Nešić, X. Wang, and P. Westling, “High-resolution stereo datasets with subpixel-accurate ground truth,” in GCPR , 2014
2014
Earlier work this paper cites.
M. Menze and A. Geiger, “Object scene flow for autonomous vehicles,” in CVPR , 2015
2015
Earlier work this paper cites.
N. Mayer, E. Ilg, P. Hausser, P. Fischer, D. Cremers, A. Dosovitskiy, and T. Brox, “A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation,” in CVPR , 2016
2016
Earlier work this paper cites.
A. Kendall, H. Martirosyan, S. Dasgupta, P. Henry, R. Kennedy, A. Bachrach, and A. Bry, “End-to-end learning of geometry and context for deep stereo regression,” in ICCV , 2017
2017
Earlier work this paper cites.
C. Peng, X. Zhang, G. Yu, G. Luo, and J. Sun, “Large kernel matters–improve semantic segmentation by global convolutional network,” in CVPR , 2017
2017
Earlier work this paper cites.
J.-R. Chang and Y.-S. Chen, “Pyramid stereo matching network,” in CVPR , 2018
2018
Earlier work this paper cites.
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “Mobilenetv2: Inverted residuals and linear bottlenecks,” in CVPR , 2018
2018
Earlier work this paper cites.
S. Khamis, S. Fanello, C. Rhemann, A. Kowdle, J. Valentin, and S. Izadi, “Stereonet: Guided hierarchical refinement for real-time edge-aware depth prediction,” in ECCV , 2018
2018
Earlier work this paper cites.
S. Khamis, S. Fanello, C. Rhemann, A. Kowdle, J. Valentin, and S. Izadi, “Stereonet: Guided hierarchical refinement for real-time edge-aware depth prediction,” in ECCV , 2018
2018
Earlier work this paper cites.
J. Hu, L. Shen, and G. Sun, “Squeeze-and-excitation networks,” in CVPR , 2018
2018
Earlier work this paper cites.
G. Yang, H. Zhao, J. Shi, Z. Deng, and J. Jia, “Segstereo: Exploiting semantic information for disparity estimation,” in ECCV , 2018
2018
Earlier work this paper cites.
X. Guo, K. Yang, W. Yang, X. Wang, and H. Li, “Group-wise correlation stereo network,” in CVPR , 2019
2019
Earlier work this paper cites.
F. Zhang, V. Prisacariu, R. Yang, and P. H. Torr, “Ga-net: Guided aggregation net for end-to-end stereo matching,” in CVPR , 2019
2019
Earlier work this paper cites.
G.-Y. Nie, M.-M. Cheng, Y. Liu, Z. Liang, D.-P. Fan, Y. Liu, and Y. Wang, “Multi-level context ultra-aggregation for stereo matching,” in CVPR , 2019
2019
Earlier work this paper cites.
S. Duggal, S. Wang, W.-C. Ma, R. Hu, and R. Urtasun, “Deeppruner: Learning efficient stereo matching via differentiable patchmatch,” in ICCV , 2019
2019
Earlier work this paper cites.
Y. Wang, Z. Lai, G. Huang, B. H. Wang, L. van der Maaten, M. Campbell, and K. Q. Weinberger, “Anytime stereo image depth estimation on mobile devices,” in ICRA , 2019
2019
Earlier work this paper cites.
A. Tonioni, F. Tosi, M. Poggi, S. Mattoccia, and L. D. Stefano, “Real-time self-adaptive deep stereo,” in CVPR , 2019
2019
Earlier work this paper cites.
A. Howard, M. Sandler, G. Chu, L.-C. Chen, B. Chen, M. Tan, W. Wang, Y. Zhu, R. Pang, V. Vasudevan et al. , “Searching for mobilenetv3,” in ICCV , 2019
2019
Cited alongside, same era.
Z. Wu, X. Wu, X. Zhang, S. Wang, and L. Ju, “Semantic stereo matching with pyramid cost volumes,” in ICCV , 2019
2019
Cited alongside, same era.
F. Zhang, X. Qi, R. Yang, V. Prisacariu, B. Wah, and P. Torr, “Domain-invariant stereo matching networks,” in ECCV , 2020
2020
Cited alongside, same era.
X. Gu, Z. Fan, S. Zhu, Z. Dai, F. Tan, and P. Tan, “Cascade cost volume for high-resolution multi-view stereo and stereo matching,” in CVPR , 2020
2020
Cited alongside, same era.
X. Cheng, Y. Zhong, M. Harandi, Y. Dai, X. Chang, H. Li, T. Drummond, and Z. Ge, “Hierarchical neural architecture search for deep stereo matching,” in NeurIPS , 2020
2020
J. Li, P. Wang, P. Xiong, T. Cai, Z. Yan, L. Yang, J. Liu, H. Fan, and S. Liu, “Practical stereo matching via cascaded recurrent network with adaptive correlation,” in CVPR , 2022
2022
Later among the works it cites.
B. Liu, H. Yu, and Y. Long, “Local similarity pattern and cost self-reassembling for deep stereo matching networks,” in AAAI , 2022
2022
Later among the works it cites.
G. Xu, J. Cheng, P. Guo, and X. Yang, “Attention concatenation volume for accurate and efficient stereo matching,” in CVPR , 2022
2022
Later among the works it cites.
F. Shamsafar, S. Woerz, R. Rahim, and A. Zell, “Mobilestereonet: Towards lightweight deep networks for stereo matching,” in WACV , 2022
2022
Later among the works it cites.
M.-H. Guo, C.-Z. Lu, Q. Hou, Z. Liu, M.-M. Cheng, and S.-M. Hu, “Segnext: Rethinking convolutional attention design for semantic segmentation,” NeurIPS , 2022
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Cited alongside, same era.
Q. Wang, S. Shi, S. Zheng, K. Zhao, and X. Chu, “FADNet: A fast and accurate network for disparity estimation,” in ICRA , 2020
2020
Cited alongside, same era.
H. Xu and J. Zhang, “Aanet: Adaptive aggregation network for efficient stereo matching,” in CVPR , 2020
2020
Cited alongside, same era.
Q. Hou, L. Zhang, M.-M. Cheng, and J. Feng, “Strip pooling: Rethinking spatial pooling for scene parsing,” in CVPR , 2020
2020
Cited alongside, same era.
X. Song, X. Zhao, L. Fang, H. Hu, and Y. Yu, “Edgestereo: An effective multi-task learning network for stereo matching and edge detection,” IJCV , 2020
2020
Cited alongside, same era.
H. Wang, R. Fan, P. Cai, and M. Liu, “Pvstereo: Pyramid voting module for end-to-end self-supervised stereo matching,” ICRA , 2021
2021
Cited alongside, same era.
X. Song, G. Yang, X. Zhu, H. Zhou, Z. Wang, and J. Shi, “Adastereo: A simple and efficient approach for adaptive stereo matching,” in CVPR , 2021
2021
Cited alongside, same era.
2021
Cited alongside, same era.
2022
Later among the works it cites.
B. Liu, H. Yu, and Y. Long, “Local similarity pattern and cost self-reassembling for deep stereo matching networks,” in AAAI , 2022
2022
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
M. M. Mijwil, R. Doshi, K. K. Hiran, O. J. Unogwu, and I. Bala, “Mobilenetv1-based deep learning model for accurate brain tumor classification,” Mesopotamian Journal of Computer Science , 2023
2023
Later among the works it cites.
G. Xu, X. Wang, X. Ding, and X. Yang, “Iterative geometry encoding volume for stereo matching,” in CVPR , 2023
2023
Later among the works it cites.
X. Liu, H. Peng, N. Zheng, Y. Yang, H. Hu, and Y. Yuan, “Efficientvit: Memory efficient vision transformer with cascaded group attention,” in CVPR , 2023
2023
Later among the works it cites.
G. Xu, Y. Wang, J. Cheng, J. Tang, and X. Yang, “Accurate and efficient stereo matching via attention concatenation volume,” TPAMI , 2023
2023
Later among the works it cites.
2024
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X. Wang, G. Xu, H. Jia, and X. Yang, “Selective-stereo: Adaptive frequency information selection for stereo matching,” in CVPR , 2024
2024
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P. Xu, Z. Xiang, C. Qiao, J. Fu, and T. Pu, “Adaptive multi-modal cross-entropy loss for stereo matching,” in CVPR , 2024
2024
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M. Poggi and F. Tosi, “Federated online adaptation for deep stereo,” in CVPR , 2024
2024
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X. Li, C. Zhang, W. Su, and W. Tao, “Iinet: Implicit intra-inter information fusion for real-time stereo matching,” in AAAI , 2024
2024
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X. Ma, X. Dai, Y. Bai, Y. Wang, and Y. Fu, “Rewrite the stars,” CVPR , 2024
2024
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