Page 103 - Read Online
P. 103
Li et al. Intell Robot 2021;1(1):84-98 I http://dx.doi.org/10.20517/ir.2021.06 Page 98
12. Godard C, Mac Aodha O, Brostow GJ. Unsupervised monocular depth estimation with leftright consistency. In: Proceedings of the IEEE
Conference on Computer Vision and Pattern Recognition; 2017. pp. 270–79.
13. Zhan H, Garg R, Weerasekera CS, et al. Unsupervised learning of monocular depth estimation and visual odometry with deep
feature reconstruction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2018. pp. 340–49.
14. Li R, Wang S, Long Z, Gu D. Undeepvo: Monocular visual odometry through unsupervised deep learning. In: 2018 IEEE
International Conference on Robotics and Automation (ICRA). IEEE; 2018. pp. 7286–91.
15. Poggi M, Aleotti F, Tosi F, Mattoccia S. Towards realtime unsupervised monocular depth estimation on cpu. In: 2018 IEEE/RSJ
International Conference on Intelligent Robots and Systems (IROS). IEEE; 2018. pp. 5848–54.
16. Zhou T, Brown M, Snavely N, Lowe DG. Unsupervised learning of depth and egomotion from video. In: Proceedings of the IEEE
Conference on Computer Vision and Pattern Recognition; 2017. pp. 1851–58.
17. Casser V, Pirk S, Mahjourian R, Angelova A. Depth prediction without the sensors: Leveraging structure for unsupervised learning from
monocular videos. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 33; 2019. pp. 8001–8.
18. Yin Z, Shi J. Geonet: Unsupervised learning of dense depth, optical flow and camera pose. In: Proceedings of the IEEE Conference
on Computer Vision and Pattern Recognition; 2018. pp. 1983–92.
19. Luo C, Yang Z, Wang P, et al. Every pixel counts++: Joint learning of geometry and motion with 3d holistic understanding.
IEEE Trans Pattern Anal Mach Intell 2019;42:2624–41.
20. Xie S, Girshick R, Dollár P, Tu Z, He K. Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE
Conference on Computer Vision and Pattern Recognition; 2017. pp. 1492–500.
21. Yang HH, Yang CHH, Tsai YCJ. Ynet: Multiscale feature aggregation network with wavelet structure similarity loss function for single
image dehazing. In: ICASSP 20202020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE;
2020. pp. 2628–32.
22. Godard C, Mac Aodha O, Firman M, Brostow GJ. Digging into selfsupervised monocular depth estimation. In: Proceedings of the
IEEE/CVF International Conference on Computer Vision; 2019. pp. 3828–38.
23. Wang C, Buenaposada JM, Zhu R, Lucey S. Learning depth from monocular videos using direct methods. In: Proceedings of the IEEE
Conference on Computer Vision and Pattern Recognition; 2018. pp. 2022–30.
24. Ranjan A, Jampani V, Balles L, et al. Competitive collaboration: joint unsupervised learning of depth, camera motion, optical flow
and motion segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition; 2019. pp.
12240–49.
25. Nair V, Hinton GE. Rectified linear units improve restricted boltzmann machines. In: Icml; 2010.
26. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. Image quality assessment: from error visibility to structural similarity. IEEE
Trans Image Process 2004;13:600–612.
27. Ketkar N. Introduction to pytorch. In: Deep learning with python. Springer; 2017. pp. 195–208.
28. Kingma DP, Ba J. Adam: a method for stochastic optimization. arXiv preprint arXiv:14126980 2014.
29. Yang Z, Wang P, Xu W, Zhao L, Nevatia R. Unsupervised learning of geometry with edgeaware depthnormal consistency. arXiv preprint
arXiv:171103665 2017.
30. Mahjourian R, Wicke M, Angelova A. Unsupervised learning of depth and egomotion from monocular video using 3d geometric con
straints. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2018. pp. 5667–75.
31. Zou Y, Luo Z, Huang JB. Dfnet: Unsupervised joint learning of depth and flow using crosstask consistency. In: Proceedings of the
European Conference on Computer Vision (ECCV); 2018. pp. 36–53.
32. Yang Z, Wang P, Wang Y, Xu W, Nevatia R. Lego: Learning edge with geometry all at once by watching videos. In: Proceedings of the
IEEE Conference on Computer Vision and Pattern Recognition; 2018. pp. 225–34.
33. MurArtal R, Montiel JMM, Tardos JD. ORBSLAM: a versatile and accurate monocular SLAM system. IEEE T ROBOT 2015;31:
1147–63.