Fetching the paper…
Reading the bibliography…
Estimating the distance to objects is crucial for autonomous vehicles when using depth sensors is not possible.
R. Collins, “A space-sweep approach to true multi-image matching,” in IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) , San Francisco, California, USA, June 1996, pp. 358–363. [Online]. Available: https://doi.org/10.1109/CVPR.1996.517097
1996
Earlier work this paper cites.
A. Saxena, S. Chung, and A. Ng, “Learning depth from single monocular images,” in Advances in Neural Information Processing Systems (NeurIPS) , vol. 18. MIT Press, 2005, pp. 1161–1168
2005
Earlier work this paper cites.
D. Gallup, J. Frahm, P. Mordohai, Q. Yang, and M. Pollefeys, “Real-time plane-sweeping stereo with multiple sweeping directions,” in IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) , Minneapolis, MN, USA, June 2007, pp. 1–8. [Online]. Available: https://doi.org/10.1109/CVPR.2007.383245
2007
Earlier work this paper cites.
M. Achtelik, A. Bachrach, R. He, S. Prentice, and N. Roy, “Stereo vision and laser odometry for autonomous helicopters in GPS-denied indoor environments,” in Unmanned Systems Technology XI , vol. 7332, International Society for Optics and Photonics. SPIE, 2009, pp. 336–345. [Online]. Available: https://doi.org/10.1117/12.819082
2009
Earlier work this paper cites.
G. Dudek and M. Jenkin, Computational Principles of Mobile Robotics , 2nd ed. Cambridge University Press, 2010
2010
Earlier work this paper cites.
A. Geiger, P. Lenz, and R. Urtasun, “Are we ready for autonomous driving? The KITTI vision benchmark suite,” in IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) , Providence, Rhode Island, USA, June 2012, pp. 3354–3361. [Online]. Available: https://doi.org/10.1109/CVPR.2012.6248074
2012
Earlier work this paper cites.
2012
Earlier work this paper cites.
J. Sturm, N. Engelhard, F. Endres, W. Burgard, and D. Cremers, “A benchmark for the evaluation of RGB-D SLAM systems,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , Vilamoura, Portugal, October 2012, pp. 573–580. [Online]. Available: https://doi.org/10.1109/IROS.2012.6385773
2012
Earlier work this paper cites.
D. Eigen, C. Puhrsch, and R. Fergus, “Depth map prediction from a single image using a multi-scale deep network,” in Advances in Neural Information Processing Systems (NeurIPS) , 2014, pp. 2366–2374
2014
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional networks for biomedical image segmentation,” in Medical Image Computing and Computer-Assisted Intervention (MICCAI) , ser. Lecture Notes in Computer Science, vol. 9351. Springer, 2015, pp. 234–241. [Online]. Available: https://doi.org/10.1007/978-3-319-24574-4_28
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
A. Dosovitskiy, P. Fischer, E. Ilg, P. Hausser, C. Hazirbas, V. Golkov, P. v. d. Smagt, D. Cremers, and T. Brox, “FlowNet: Learning optical flow with convolutional networks,” in IEEE International Conference on Computer Vision (ICCV) , Santiago, Chile, December 2015, pp. 2758–2766. [Online]. Available: https://doi.org/10.1109/ICCV.2015.316
2015
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification,” in IEEE International Conference on Computer Vision (ICCV) , Santiago, Chile, December 2015, pp. 1026–1034. [Online]. Available: https://doi.org/10.1109/ICCV.2015.123
2015
Earlier work this paper cites.
D. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in International Conference on Learning Representations (ICLR) , San Diego, California, USA, May 2015, pp. 1–15
2015
Earlier work this paper cites.
G. Ros, L. Sellart, J. Materzynska, D. Vazquez, and A. M. Lopez, “The SYNTHIA dataset: A large collection of synthetic images for semantic segmentation of urban scenes,” in IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) , Las Vegas, Nevada, USA, June 2016, pp. 3234–3243. [Online]. Available: https://doi.org/10.1109/CVPR.2016.352
2016
Earlier work this paper cites.
C. Godard, O. Mac Aodha, and G. J. Brostow, “Unsupervised monocular depth estimation with left-right consistency,” in IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) , Honolulu, Hawaii, USA, July 2017, pp. 6602–6611. [Online]. Available: https://doi.org/10.1109/CVPR.2017.699
2017
Earlier work this paper cites.
O. Özyeşil, V. Voroninski, R. Basri, and A. Singer, “A survey of structure from motion,” Acta Numerica , vol. 26, pp. 305–364, May 2017. [Online]. Available: https://doi.org/10.1017/S096249291700006X
2017
Earlier work this paper cites.
B. Ummenhofer, H. Zhou, J. Uhrig, N. Mayer, E. Ilg, A. Dosovitskiy, and T. Brox, “DeMoN: Depth and motion network for learning monocular stereo,” in IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) , Honolulu, Hawaii, USA, July 2017, pp. 5622–5631. [Online]. Available: https://doi.org/10.1109/CVPR.2017.596
2017
Earlier work this paper cites.
J. Xu, R. Ranftl, and V. Koltun, “Accurate optical flow via direct cost volume processing,” in IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) , Honolulu, Hawaii, USA, July 2017, pp. 5807–5815. [Online]. Available: https://doi.org/10.1109/CVPR.2017.615
2017
Cited alongside, same era.
C. Godard, O. Mac Aodha, and G. J. Brostow, “Unsupervised monocular depth estimation with left-right consistency,” https://github.com/mrharicot/monodepth , 2017
2017
Cited alongside, same era.
M. Poggi, F. Aleotti, F. Tosi, and S. Mattoccia, “Towards real-time unsupervised monocular depth estimation on CPU,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , Madrid, Spain, October 2018, pp. 5848–5854. [Online]. Available: https://doi.org/10.1109/IROS.2018.8593814
2018
Cited alongside, same era.
A. Kumar, S. Bhandarkar, and M. Prasad, “DepthNet: A recurrent neural network architecture for monocular depth prediction,” in IEEE International Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) , Salt Lake City, Utah, USA, June 2018, pp. 396–404. [Online]. Available: https://doi.org/10.1109/CVPRW.2018.00066
——, “Digging into self-supervised monocular depth prediction,” https://github.com/nianticlabs/monodepth2 , 2019
2019
Later among the works it cites.
R. Wang, S. Pizer, and J.-M. Frahm, “Recurrent neural network for (un-)supervised learning of monocular videovisual odometry and depth,” https://github.com/wrlife/RNN_depth_pose , 2019
2019
Later among the works it cites.
R. Ranftl, K. Lasinger, D. Hafner, K. Schindler, and V. Koltun, “Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 44, no. 3, pp. 1623–1637, March 2022. [Online]. Available: https://doi.org/10.1109/TPAMI.2020.3019967
2020
Later among the works it cites.
W. Wang, D. Zhu, X. Wang, Y. Hu, Y. Qiu, C. Wang, Y. Hu, A. Kapoor, and S. Scherer, “TartanAir: A dataset to push the limits of visual SLAM,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , Las Vegas, Nevada, USA, October 2020, pp. 4909–4916. [Online]. Available: https://doi.org/10.1109/IROS45743.2020.9341801
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2018
Cited alongside, same era.
R. Mahjourian, M. Wicke, and A. Angelova, “Unsupervised learning of depth and ego-motion from monocular video using 3D geometric constraints,” in IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) , Salt Lake City, Utah, USA, June 2018, pp. 5667–5675. [Online]. Available: https://doi.org/10.1109/CVPR.2018.00594
2018
Cited alongside, same era.
Y. Yao, Z. Luo, S. Li, T. Fang, and L. Quan, “MVSNet: Depth inference for unstructured multi-view stereo,” in European Conference on Computer Vision (ECCV) , ser. Lecture Notes in Computer Science, vol. 11212. Springer, 2018, pp. 785–801. [Online]. Available: https://doi.org/10.1007/978-3-030-01237-3_47
2018
Cited alongside, same era.
2018
Cited alongside, same era.
D. Sun, X. Yang, M. Liu, and J. Kautz, “PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume,” in IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) , Salt Lake City, Utah, USA, June 2018, pp. 8934–8943. [Online]. Available: https://doi.org/10.1109/CVPR.2018.00931
2018
Cited alongside, same era.
M. Carvalho, B. Le Saux, P. Trouvé-Peloux, A. Almansa, and F. Champagnat, “On regression losses for deep depth estimation,” in IEEE International Conference on Image Processing (ICIP) , Athens, Greece, October 2018, pp. 2915–2919. [Online]. Available: https://doi.org/10.1109/ICIP.2018.8451312
2018
Cited alongside, same era.
M. Fonder and M. Van Droogenbroeck, “Mid-air: A multi-modal dataset for extremely low altitude drone flights,” in IEEE International Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), UAVision , Long Beach, California, USA, June 2019, pp. 553–562. [Online]. Available: https://doi.org/10.1109/CVPRW.2019.00081
2019
Cited alongside, same era.
——, “Digging into self-supervised monocular depth estimation,” in IEEE International Conference on Computer Vision (ICCV) , Seoul, South Korea, October-November 2019, pp. 3827–3837. [Online]. Available: https://doi.org/10.1109/ICCV.2019.00393
2019
Cited alongside, same era.
R. Wang, S. Pizer, and J.-M. Frahm, “Recurrent neural network for (un-)supervised learning of monocular video visual odometry and depth,” in IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) , Long Beach, California, USA, June 2019, pp. 5550–5559. [Online]. Available: https://doi.org/10.1109/CVPR.2019.00570
2019
Cited alongside, same era.
2020
Later among the works it cites.
Y. Ming, X. Meng, C. Fan, and H. Yu, “Deep learning for monocular depth estimation: A review,” Neurocomputing , vol. 438, pp. 14–33, May 2021. [Online]. Available: https://doi.org/10.1016/j.neucom.2020.12.089
2020
Later among the works it cites.
R. Xiaogang, Y. Wenjing, H. Jing, G. Peiyuan, and G. Wei, “Monocular depth estimation based on deep learning: A survey,” in Chinese Automation Congress (CAC) , Shanghai, China, November 2020, pp. 2436–2440. [Online]. Available: https://doi.org/10.1109/CAC51589.2020.9327548
2020
Later among the works it cites.
C. Zhao, Q. Sun, C. Zhang, Y. Tang, and F. Qian, “Monocular depth estimation based on deep learning: An overview,” Science China Technological Sciences , vol. 63, no. 9, pp. 1612–1627, June 2020. [Online]. Available: https://doi.org/10.1007/s11431-020-1582-8
2020
Later among the works it cites.
V. Patil, W. Van Gansbeke, D. Dai, and L. Van Gool, “Don’t forget the past: Recurrent depth estimation from monocular video,” IEEE Robotics and Automation Letters , vol. 5, no. 4, pp. 6813–6820, October 2020. [Online]. Available: https://doi.org/10.1109/LRA.2020.3017478
2020
Later among the works it cites.
X. Luo, J. Huang, R. Szeliski, K. Matzen, and J. Kopf, “Consistent video depth estimation,” ACM Transactions on Graphics (TOG) , vol. 39, no. 4, pp. 71:1–71–13, July 2020. [Online]. Available: https://doi.org/10.1145/3386569.3392377
2020
Later among the works it cites.
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 IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) , Seattle, Washington, USA, June 2020, pp. 2495–2504. [Online]. Available: https://doi.org/10.1109/CVPR42600.2020.00257
2020
Later among the works it cites.
F. Zhang, X. Qi, R. Yang, V. Prisacariu, B. Wah, and P. Torr, “Domain-invariant stereo matching networks,” in European Conference on Computer Vision (ECCV) , ser. Lecture Notes in Computer Science, vol. 12347. Springer, 2020, pp. 420–439. [Online]. Available: https://doi.org/10.1007/978-3-030-58536-5_25
2020
Later among the works it cites.
X. Weihao, “Exploiting temporal consistency for real-time video depth estimation - unofficial implementation,” https://github.com/weihaox/ST-CLSTM , 2020
2020
Later among the works it cites.
R. Ranftl, A. Bochkovskiy, and V. Koltun, “Vision transformers for dense prediction,” in IEEE International Conference on Computer Vision (ICCV) , Montréal, Canada, October 2021, pp. 12 159–12 168. [Online]. Available: https://doi.org/10.1109/iccv48922.2021.01196
2021
Closest in time.
J. Watson, O. Mac Aodha, V. Prisacariu, G. Brostow, and M. Firman, “The temporal opportunist: Self-supervised multi-frame monocular depth,” in IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) , Nashville, Tennessee, USA, June 2021, pp. 1164–1174. [Online]. Available: https://doi.org/10.1109/CVPR46437.2021.00122
2021
Closest in time.
J. Liu, X. Zhang, Z. Li, and T. Mao, “Multi-scale residual pyramid attention network for monocular depth estimation,” in IEEE International Conference on Pattern Recognition (ICPR) , Milan, Italy, January 2021, pp. 5137–5144. [Online]. Available: https://doi.org/10.1109/ICPR48806.2021.9412670
2021
Closest in time.
V.-C. Miclea and S. Nedevschi, “Monocular depth estimation with improved long-range accuracy for UAV environment perception,” IEEE Transactions on Geosciences and Remote Sensing , vol. 60, pp. 1–15, 2022. [Online]. Available: https://doi.org/10.1109/TGRS.2021.3060513
2021
Closest in time.