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Multi-view geometry-based methods dominate the last few decades in monocular Visual Odometry for their superior performance, while they have been vulnerable to dynamic and low-texture scenes.
Self-Supervised 3D Keypoint Learning for Ego-motion Estimation
Tang, J., Ambrus, R., Guizilini, V., Pillai, S., Kim, H., & Gaidon, A. (2019) · 1912
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Self-Supervised 3D Keypoint Learning for Ego-motion Estimation
Tang, J., Ambrus, R., Guizilini, V., Pillai, S., Kim, H., & Gaidon, A. (2019) · 1912
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The interpretation of structure from motion
Ullman, S. (1979) · 1979
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The interpretation of structure from motion
Ullman, S. (1979) · 1979
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Least-squares estimation of transformation parameters between two point patterns
Umeyama, S. (1991) · 1991
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Least-squares estimation of transformation parameters between two point patterns
Umeyama, S. (1991) · 1991
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Geonet: Unsupervised learning of dense depth, optical flow and camera pose
Yin, Z., & Shi, J. (2018) · 1992
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Geonet: Unsupervised learning of dense depth, optical flow and camera pose
Yin, Z., & Shi, J. (2018) · 1992
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In defence of the 8-point algorithm
Hartley, R. I. (1995) · 1995
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In defence of the 8-point algorithm
Hartley, R. I. (1995) · 1995
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Determining the epipolar geometry and its uncertainty: A review
Zhang, Z. (1998) · 1998
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Determining the epipolar geometry and its uncertainty: A review
Zhang, Z. (1998) · 1998
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The problem of degeneracy in structure and motion recovery from uncalibrated image sequences
Torr, P. H., Fitzgibbon, A. W., & Zisserman, A. (1999) · 1999
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The problem of degeneracy in structure and motion recovery from uncalibrated image sequences
Torr, P. H., Fitzgibbon, A. W., & Zisserman, A. (1999) · 1999
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Multiple View Geometry in Computer Vision
Hartley, R., & Zisserman, A. (2003) · 2003
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An efficient solution to the five-point relative pose problem
Nister, D. (2003) · 2003
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Multiple View Geometry in Computer Vision
Hartley, R., & Zisserman, A. (2003) · 2003
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An efficient solution to the five-point relative pose problem
Nister, D. (2003) · 2003
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Distinctive image features from scale-invariant keypoints
Lowe, D. G. (2004) · 2004
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Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · 2004
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Distinctive image features from scale-invariant keypoints
Lowe, D. G. (2004) · 2004
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Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · 2004
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Vdo-slam: A visual dynamic object-aware slam system
Zhang, J., Henein, M., Mahony, R., & Ila, V. (2020) · 2005
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Vdo-slam: A visual dynamic object-aware slam system
Zhang, J., Henein, M., Mahony, R., & Ila, V. (2020) · 2005
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Parallel tracking and mapping for small ar workspaces
Klein, G., & Murray, D. (2007) · 2007
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Computer vision on mars
Matthies, L., Maimone, M., Johnson, A., Cheng, Y., Willson, R., Villalpando, C., et al. (2007) · 2007
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Parallel tracking and mapping for small ar workspaces
Klein, G., & Murray, D. (2007) · 2007
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Computer vision on mars
Matthies, L., Maimone, M., Johnson, A., Cheng, Y., Willson, R., Villalpando, C., et al. (2007) · 2007
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Deep ordinal regression network for monocular depth estimation
Fu, H., Gong, M., Wang, C., Batmanghelich, K., & Tao, D. (2018) · 2011
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Stereoscan: Dense 3d reconstruction in real-time
Geiger, A., Ziegler, J., & Stiller, C. (2011) · 2011
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Dtam: Dense tracking and mapping in real-time
Newcombe, R. A., Lovegrove, S. J., & Davison, A. J. (2011) · 2011
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Orb: An efficient alternative to sift or surf
Rublee, E., Rabaud, V., Konolige, K., & Bradski, G. (2011) · 2011
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Visual odometry: Part i: The first 30 years and fundamentals
Scaramuzza, D., & Fraundorfer, F. (2011) · 2011
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Deep ordinal regression network for monocular depth estimation
Fu, H., Gong, M., Wang, C., Batmanghelich, K., & Tao, D. (2018) · 2011
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Stereoscan: Dense 3d reconstruction in real-time
Geiger, A., Ziegler, J., & Stiller, C. (2011) · 2011
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Dtam: Dense tracking and mapping in real-time
Newcombe, R. A., Lovegrove, S. J., & Davison, A. J. (2011) · 2011
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Orb: An efficient alternative to sift or surf
Rublee, E., Rabaud, V., Konolige, K., & Bradski, G. (2011) · 2011
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Visual odometry: Part i: The first 30 years and fundamentals
Scaramuzza, D., & Fraundorfer, F. (2011) · 2011
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Are we ready for autonomous driving? the kitti vision benchmark suite
Geiger, A., Lenz, P., & Urtasun, R. (2012) · 2012
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Are we ready for autonomous driving? the kitti vision benchmark suite
Geiger, A., Lenz, P., & Urtasun, R. (2012) · 2012
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Vision meets robotics: The kitti dataset
Geiger, A., Lenz, P., Stiller, C., & Urtasun, R. (2013) · 2013
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Vision meets robotics: The kitti dataset
Geiger, A., Lenz, P., Stiller, C., & Urtasun, R. (2013) · 2013
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Depth map prediction from a single image using a multi-scale deep network
Eigen, D., Puhrsch, C., & Fergus, R. (2014) · 2014
Cited alongside, same era.
LSD-SLAM: Large-scale direct monocular slam
Engel, J., Schöps, T., & Cremers, D. (2014) · 2014
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Svo: Fast semi-direct monocular visual odometry
Forster, C., Pizzoli, M., & Scaramuzza, D. (2014) · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, D., & Ba, J. (2014) · 2014
Cited alongside, same era.
Depth map prediction from a single image using a multi-scale deep network
Eigen, D., Puhrsch, C., & Fergus, R. (2014) · 2014
Automatic differentiation in PyTorch
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., et al. (2017) · 2017
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CNN-SLAM: Real-time dense monocular slam with learned depth prediction
Tateno, K., Tombari, F., Laina, I., & Navab, N. (2017) · 2017
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Demon: Depth and motion network for learning monocular stereo
Ummenhofer, B., Zhou, H., Uhrig, J., Mayer, N., Ilg, E., Dosovitskiy, A., et al. (2017) · 2017
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Unsupervised learning of depth and ego-motion from video
Zhou, T., Brown, M., Snavely, N., & Lowe, D. G. (2017) · 2017
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Eng: End-to-end neural geometry for robust depth and pose estimation using cnns
Dharmasiri, T., Spek, A., & Drummond, T. (2018) · 2018
Later among the works it cites.
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Cited alongside, same era.
LSD-SLAM: Large-scale direct monocular slam
Engel, J., Schöps, T., & Cremers, D. (2014) · 2014
Cited alongside, same era.
Svo: Fast semi-direct monocular visual odometry
Forster, C., Pizzoli, M., & Scaramuzza, D. (2014) · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, D., & Ba, J. (2014) · 2014
Cited alongside, same era.
Learning to see by moving
Agrawal, P., Carreira, J., & Malik, J. (2015) · 2015
Cited alongside, same era.
Flownet: Learning optical flow with convolutional networks
Dosovitskiy, A., Fischer, P., Ilg, E., Hausser, P., Hazirbas, C., Golkov, V., et al. (2015) · 2015
Cited alongside, same era.
Deep convolutional neural fields for depth estimation from a single image
Liu, F., Shen, C., & Lin, G. (2015) · 2015
Cited alongside, same era.
Hui, T.-W., Tang, X., & Loy, C. C. (2018) · 2018
Later among the works it cites.
Unflow: Unsupervised learning of optical flow with a bidirectional census loss
Meister, S., Hur, J., & Roth, S. (2018) · 2018
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Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume
Sun, D., Yang, X., Liu, M.-Y., & Kautz, J. (2018) · 2018
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Deep virtual stereo odometry: Leveraging deep depth prediction for monocular direct sparse odometry
Yang, N., Wang, R., Stueckler, J., & Cremers, D. (2018) · 2018
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Unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction
Zhan, H., Garg, R., Weerasekera, C. S., Li, K., Agarwal, H., & Reid, I. (2018) · 2018
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Deeptam: Deep tracking and mapping
Zhou, H., Ummenhofer, B., & Brox, T. (2018) · 2018
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Eng: End-to-end neural geometry for robust depth and pose estimation using cnns
Dharmasiri, T., Spek, A., & Drummond, T. (2018) · 2018
Later among the works it cites.
Liteflownet: A lightweight convolutional neural network for optical flow estimation
Hui, T.-W., Tang, X., & Loy, C. C. (2018) · 2018
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Unflow: Unsupervised learning of optical flow with a bidirectional census loss
Meister, S., Hur, J., & Roth, S. (2018) · 2018
Later among the works it cites.
Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume
Sun, D., Yang, X., Liu, M.-Y., & Kautz, J. (2018) · 2018
Later among the works it cites.
Deep virtual stereo odometry: Leveraging deep depth prediction for monocular direct sparse odometry
Yang, N., Wang, R., Stueckler, J., & Cremers, D. (2018) · 2018
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Unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction
Zhan, H., Garg, R., Weerasekera, C. S., Li, K., Agarwal, H., & Reid, I. (2018) · 2018
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Deeptam: Deep tracking and mapping
Zhou, H., Ummenhofer, B., & Brox, T. (2018) · 2018
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Digging into self-supervised monocular depth prediction
Godard, C., Mac Aodha, O., Firman, M., & Brostow, G. J. (2019) · 2019
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Pose graph optimization for unsupervised monocular visual odometry
Li, Y., Ushiku, Y., & Harada, T. (2019) · 2019
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CNN-SVO: Improving the mapping in semi-direct visual odometry using single-image depth prediction
Loo, S. Y., Amiri, A. J., Mashohor, S., Tang, S. H., & Zhang, H. (2019) · 2019
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Real-time joint semantic segmentation and depth estimation using asymmetric annotations
Nekrasov, V., Dharmasiri, T., Spek, A., Drummond, T., Shen, C., & Reid, I. (2019) · 2019
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Competitive collaboration: Joint unsupervised learning of depth, camera motion, optical flow and motion segmentation
Ranjan, A., Jampani, V., Balles, L., Kim, K., Sun, D., Wulff, J., et al. (2019) · 2019
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Self-supervised learning for single view depth and surface normal estimation
Zhan, H., Weerasekera, C. S., Garg, R., & Reid, I. D. (2019) · 2019
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Ground-plane-based absolute scale estimation for monocular visual odometry
Zhou, D., Dai, Y., & Li, H. (2019) · 2019
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Digging into self-supervised monocular depth prediction
Godard, C., Mac Aodha, O., Firman, M., & Brostow, G. J. (2019) · 2019
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Pose graph optimization for unsupervised monocular visual odometry
Li, Y., Ushiku, Y., & Harada, T. (2019) · 2019
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CNN-SVO: Improving the mapping in semi-direct visual odometry using single-image depth prediction
Loo, S. Y., Amiri, A. J., Mashohor, S., Tang, S. H., & Zhang, H. (2019) · 2019
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Real-time joint semantic segmentation and depth estimation using asymmetric annotations
Nekrasov, V., Dharmasiri, T., Spek, A., Drummond, T., Shen, C., & Reid, I. (2019) · 2019
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Competitive collaboration: Joint unsupervised learning of depth, camera motion, optical flow and motion segmentation
Ranjan, A., Jampani, V., Balles, L., Kim, K., Sun, D., Wulff, J., et al. (2019) · 2019
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Self-supervised learning for single view depth and surface normal estimation
Zhan, H., Weerasekera, C. S., Garg, R., & Reid, I. D. (2019) · 2019
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Ground-plane-based absolute scale estimation for monocular visual odometry
Zhou, D., Dai, Y., & Li, H. (2019) · 2019
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Visual odometry revisited: What should be learnt?
Zhan, H., Weerasekera, C. S., Bian, J., & Reid, I. (2020) · 2020
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Visual odometry revisited: What should be learnt?
Zhan, H., Weerasekera, C. S., Bian, J., & Reid, I. (2020) · 2020
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