Fetching the paper…
Reading the bibliography…
Accurate and dense depth estimation with stereo cameras and LiDAR is an important task for automatic driving and robotic perception.
H. Xu and J. Zhang, “Aanet: Adaptive aggregation network for efficient stereo matching,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 1959–1968
1968
Earlier work this paper cites.
L. Nalpantidis and A. Gasteratos, “Stereo vision for robotic applications in the presence of non-ideal lighting conditions,” Image and Vision Computing , vol. 28, no. 6, pp. 940–951, 2010
2010
Earlier work this paper cites.
A. Geiger, J. Ziegler, and C. Stiller, “Stereoscan: Dense 3d reconstruction in real-time,” in 2011 IEEE intelligent vehicles symposium (IV) . Ieee, 2011, pp. 963–968
2011
Earlier work this paper cites.
S. Zagoruyko and N. Komodakis, “Learning to compare image patches via convolutional neural networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 4353–4361
2015
Earlier work this paper cites.
J. Zbontar, Y. LeCun, et al. , “Stereo matching by training a convolutional neural network to compare image patches.” J. Mach. Learn. Res. , vol. 17, no. 1, pp. 2287–2318, 2016
2016
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 Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 4040–4048
2016
Earlier work this paper cites.
J. Uhrig, N. Schneider, L. Schneider, U. Franke, T. Brox, and A. Geiger, “Sparsity invariant cnns,” in 2017 international conference on 3D Vision (3DV) . IEEE, 2017, pp. 11–20
2017
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 Proceedings of the IEEE international conference on computer vision , 2017, pp. 66–75
2017
Earlier work this paper cites.
S. Liu, S. De Mello, J. Gu, G. Zhong, M.-H. Yang, and J. Kautz, “Learning affinity via spatial propagation networks,” Advances in Neural Information Processing Systems , vol. 30, 2017
2017
Earlier work this paper cites.
J. Dai, H. Qi, Y. Xiong, Y. Li, G. Zhang, H. Hu, and Y. Wei, “Deformable convolutional networks,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 764–773
2017
Earlier work this paper cites.
J.-R. Chang and Y.-S. Chen, “Pyramid stereo matching network,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 5410–5418
2018
Earlier work this paper cites.
X. Cheng, P. Wang, and R. Yang, “Depth estimation via affinity learned with convolutional spatial propagation network,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 103–119
2018
Earlier work this paper cites.
K. Park, S. Kim, and K. Sohn, “High-precision depth estimation with the 3d lidar and stereo fusion,” in 2018 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2018, pp. 2156–2163
2018
Earlier work this paper cites.
S. S. Shivakumar, K. Mohta, B. Pfrommer, V. Kumar, and C. J. Taylor, “Real time dense depth estimation by fusing stereo with sparse depth measurements,” in 2019 International Conference on Robotics and Automation (ICRA) . IEEE, 2019, pp. 6482–6488
2019
Earlier work this paper cites.
J. Qiu, Z. Cui, Y. Zhang, X. Zhang, S. Liu, B. Zeng, and M. Pollefeys, “Deeplidar: Deep surface normal guided depth prediction for outdoor scene from sparse lidar data and single color image,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 3313–3322
2019
Earlier work this paper cites.
M. Poggi, D. Pallotti, F. Tosi, and S. Mattoccia, “Guided stereo matching,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 979–988
2019
Cited alongside, same era.
T.-H. Wang, H.-N. Hu, C. H. Lin, Y.-H. Tsai, W.-C. Chiu, and M. Sun, “3d lidar and stereo fusion using stereo matching network with conditional cost volume normalization,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2019, pp. 5895–5902
2019
Cited alongside, same era.
S. S. Shivakumar, T. Nguyen, I. D. Miller, S. W. Chen, V. Kumar, and C. J. Taylor, “Dfusenet: Deep fusion of rgb and sparse depth information for image guided dense depth completion,” in 2019 IEEE Intelligent Transportation Systems Conference (ITSC) . IEEE, 2019, pp. 13–20
2019
Cited alongside, same era.
X. Cheng, Y. Zhong, Y. Dai, P. Ji, and H. Li, “Noise-aware unsupervised deep lidar-stereo fusion,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 6339–6348
2020
Later among the works it cites.
Y.-K. Huang, Y.-C. Liu, T.-H. Wu, H.-T. Su, Y.-C. Chang, T.-L. Tsou, Y.-A. Wang, and W. H. Hsu, “S3: Learnable sparse signal superdensity for guided depth estimation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 16 706–16 716
2021
Later among the works it cites.
Z. Shen, Y. Dai, and Z. Rao, “Cfnet: Cascade and fused cost volume for robust stereo matching,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 13 906–13 915
2021
Later among the works it cites.
V. Tankovich, C. Hane, Y. Zhang, A. Kowdle, S. Fanello, and S. Bouaziz, “Hitnet: Hierarchical iterative tile refinement network for real-time stereo matching,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 14 362–14 372
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2019
Cited alongside, same era.
2019
Cited alongside, same era.
X. Zhu, H. Hu, S. Lin, and J. Dai, “Deformable convnets v2: More deformable, better results,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 9308–9316
2019
Cited alongside, same era.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, et al. , “Pytorch: An imperative style, high-performance deep learning library,” Advances in neural information processing systems , vol. 32, 2019
2019
Cited alongside, same era.
T. A. Siddiqui, R. Madhok, and M. O’Toole, “An extensible multi-sensor fusion framework for 3d imaging,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops , 2020, pp. 1008–1009
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 Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 2495–2504
2020
Cited alongside, same era.
Y. Cabon, N. Murray, and M. Humenberger, “Virtual kitti 2,” arXiv preprint arXiv:2001.10773 , 2020
2020
Cited alongside, same era.
J. Park, K. Joo, Z. Hu, C.-K. Liu, and I. So Kweon, “Non-local spatial propagation network for depth completion,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XIII 16 . Springer, 2020, pp. 120–136
2020
Cited alongside, same era.
J. Tang, F.-P. Tian, W. Feng, J. Li, and P. Tan, “Learning guided convolutional network for depth completion,” IEEE Transactions on Image Processing , vol. 30, pp. 1116–1129, 2020
2020
Cited alongside, same era.
2021
Later among the works it cites.
L. Lipson, Z. Teed, and J. Deng, “Raft-stereo: Multilevel recurrent field transforms for stereo matching,” in 2021 International Conference on 3D Vision (3DV) . IEEE, 2021, pp. 218–227
2021
Later among the works it cites.
M. Hu, S. Wang, B. Li, S. Ning, L. Fan, and X. Gong, “Penet: Towards precise and efficient image guided depth completion,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 13 656–13 662
2021
Later among the works it cites.
N.-A.-M. Mai, P. Duthon, L. Khoudour, A. Crouzil, and S. Velastin, “Sparse lidar and stereo fusion (sls-fusion) for depth estimation and 3d object detection,” 2021
2021
Later among the works it cites.
J. Choe, K. Joo, T. Imtiaz, and I. S. Kweon, “Volumetric propagation network: Stereo-lidar fusion for long-range depth estimation,” IEEE Robotics and Automation Letters , vol. 6, no. 3, pp. 4672–4679, 2021
2021
Later among the works it cites.
Y. Zhang, L. Wang, K. Li, Z. Fu, and Y. Guo, “Slfnet: a stereo and lidar fusion network for depth completion,” IEEE Robotics and Automation Letters , vol. 7, no. 4, pp. 10 605–10 612, 2022
2022
Later among the works it cites.
H. Zhao, H. Zhou, Y. Zhang, Y. Zhao, Y. Yang, and T. Ouyang, “Eai-stereo: Error aware iterative network for stereo matching,” in Proceedings of the Asian Conference on Computer Vision , 2022, pp. 315–332
2022
Later among the works it cites.
Z. Xu, Y. Li, S. Zhu, and Y. Sun, “Expanding sparse lidar depth and guiding stereo matching for robust dense depth estimation,” IEEE Robotics and Automation Letters , vol. 8, no. 3, pp. 1479–1486, 2023
2023
Later among the works it cites.
U. Shin, J. Park, and I. S. Kweon, “Deep depth estimation from thermal image,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 1043–1053
2023
Later among the works it cites.
H. Xu, J. Zhang, J. Cai, H. Rezatofighi, F. Yu, D. Tao, and A. Geiger, “Unifying flow, stereo and depth estimation,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2023
2023
Later among the works it cites.
L. Liu, X. Song, J. Sun, X. Lyu, L. Li, Y. Liu, and L. Zhang, “Mff-net: Towards efficient monocular depth completion with multi-modal feature fusion,” IEEE Robotics and Automation Letters , 2023
2023
Later among the works it cites.