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In this paper, we introduce a self-supervised deep SLAM method that robustly operates in dynamic scenes while accurately identifying dynamic components.
The openCV library
Bradski, G. 2000 · 2000
Earlier work this paper cites.
Cabon, Y.; Murray, N.; and Humenberger, M. 2020 · 2001
Earlier work this paper cites.
On 3D scene flow and structure estimation
Zhang, Y.; and Kambhamettu, C. 2001 · 2001
Earlier work this paper cites.
Image quality assessment: from error visibility to structural similarity
Zhou, W.; Bovik, A. C.; Sheikh, H. R.; and Simoncelli, E. P. 2004 · 2004
Earlier work this paper cites.
ORB-SLAM3: An accurate open-source library for visual, visual-inertial and multi-map SLAM
Campos, C.; Elvira, R.; Rodríguez, J. J. G.; Montiel, J. M.; and Tardós, J. D. 2020 · 2007
Earlier work this paper cites.
A variational method for scene flow estimation from stereo sequences
Huguet, F.; and Devernay, F. 2007 · 2007
Earlier work this paper cites.
Kinecting the dots: Particle based scene flow from depth sensors
Hadfield, S.; and Bowden, R. 2011 · 2011
Earlier work this paper cites.
A benchmark for the evaluation of RGB-D SLAM systems
Sturm, J.; Engelhard, N.; Endres, F.; Burgard, W.; and Cremers, D. 2012 · 2012
Earlier work this paper cites.
Vision meets robotics: The KITTI dataset
Geiger, A.; Lenz, P.; Stiller, C.; and Urtasun, R. 2013 · 2013
Earlier work this paper cites.
Robust odometry estimation for RGB-D cameras
Kerl, C.; Sturm, J.; and Cremers, D. 2013 · 2013
Earlier work this paper cites.
Robust monocular SLAM in dynamic environments
Tan, W.; Liu, H.; Dong, Z.; Zhang, G.; and Bao, H. 2013 · 2013
Earlier work this paper cites.
Piecewise rigid scene flow
Vogel, C.; Schindler, K.; and Roth, S. 2013 · 2013
Earlier work this paper cites.
LSD-SLAM: Large-Scale Direct Monocular SLAM
Engel, J.; Schops, T.; and Cremers, D. 2014 · 2014
Earlier work this paper cites.
SVO: Fast semi-direct monocular visual odometry
Forster, C.; Pizzoli, M.; and Scaramuzza, D. 2014 · 2014
Earlier work this paper cites.
SphereFlow: 6 DoF scene flow from RGB-D pairs
Hornacek, M.; Fitzgibbon, A.; and Rother, C. 2014 · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P.; and Ba, J. 2014 · 2014
Earlier work this paper cites.
Dense semi-rigid scene flow estimation from rgbd images
Quiroga, J.; Brox, T.; Devernay, F.; and Crowley, J. 2014 · 2014
Earlier work this paper cites.
Flownet: Learning optical flow with convolutional networks
Dosovitskiy, A.; Fischer, P.; Ilg, E.; Hausser, P.; Hazirbas, C.; Golkov, V.; Van Der Smagt, P.; Cremers, D.; and Brox, T. 2015 · 2015
Earlier work this paper cites.
ORB-SLAM: a versatile and accurate monocular SLAM system
Mur-Artal, R.; Montiel, J. M. M.; and Tardos, J. D. 2015 · 2015
Earlier work this paper cites.
The EuRoC micro aerial vehicle datasets
Burri, M.; Nikolic, J.; Gohl, P.; Schneider, T.; Rehder, J.; Omari, S.; Achtelik, M. W.; and Siegwart, R. 2016 · 2016
Earlier work this paper cites.
SVO: Semidirect visual odometry for monocular and multicamera systems
Forster, C.; Zhang, Z.; Gassner, M.; Werlberger, M.; and Scaramuzza, D. 2016 · 2016
Earlier work this paper cites.
Mask R-CNN
He, K.; Gkioxari, G.; Dollar, P.; and Girshick, R. 2017 · 2017
Earlier work this paper cites.
Flownet 2.0: Evolution of optical flow estimation with deep networks
Ilg, E.; Mayer, N.; Saikia, T.; Keuper, M.; Dosovitskiy, A.; and Brox, T. 2017 · 2017
Earlier work this paper cites.
Orb-slam2: An open-source slam system for monocular, stereo, and rgb-d cameras
Mur-Artal, R.; and Tardós, J. D. 2017 · 2017
Earlier work this paper cites.
Cnn-slam: Real-time dense monocular slam with learned depth prediction
Tateno, K.; Tombari, F.; Laina, I.; and Navab, N. 2017 · 2017
Earlier work this paper cites.
Non-iterative RGB-D-inertial Odometry
Wang, C.; Hoang, M.-C.; Xie, L.; and Yuan, J. 2017 · 2017
Earlier work this paper cites.
Non-iterative SLAM
Wang, C.; Yuan, J.; and Xie, L. 2017 · 2017
Cited alongside, same era.
Unsupervised Learning of Depth and Ego-Motion from Video
Zhou, T.; Brown, M.; Snavely, N.; and Lowe, D. G. 2017 · 2017
Cited alongside, same era.
DynaSLAM: Tracking, mapping, and inpainting in dynamic scenes
Bescos, B.; Fácil, J. M.; Civera, J.; and Neira, J. 2018 · 2018
Cited alongside, same era.
Geometry-aware learning of maps for camera localization
Brahmbhatt, S.; Gu, J.; Kim, K.; Hays, J.; and Kautz, J. 2018 · 2018
Cited alongside, same era.
Semantic monocular SLAM for highly dynamic environments
Brasch, N.; Bozic, A.; Lallemand, J.; and Tombari, F. 2018 · 2018
Cited alongside, same era.
Learning Rigidity in Dynamic Scenes with a Moving Camera for 3D Motion Field Estimation
Lv, Z.; Kim, K.; Troccoli, A.; Sun, D.; Rehg, J.; and Kautz, J. 2018 · 2018
Cited alongside, same era.
Dynamic SLAM: the need for speed
Henein, M.; Zhang, J.; Mahony, R.; and Ila, V. 2020 · 2020
Later among the works it cites.
Dual-SLAM: A framework for robust single camera navigation
Huang, H.; Lin, W.-Y.; Liu, S.; Zhang, D.; and Yeung, S.-K. 2020a · 2020
Later among the works it cites.
A survey of loss functions for semantic segmentation
Jadon, S. 2020 · 2020
Later among the works it cites.
Multi-object monocular slam for dynamic environments
Nair, G. B.; Daga, S.; Sajnani, R.; Ramesh, A.; Ansari, J. A.; Jatavallabhula, K. M.; and Krishna, K. M. 2020 · 2020
Later among the works it cites.
Raft: Recurrent all-pairs field transforms for optical flow
Teed, Z.; and Deng, J. 2020 · 2020
Later among the works it cites.
Dynamic object tracking and masking for visual SLAM
Vincent, J.; Labbé, M.; Lauzon, J.-S.; Grondin, F.; Comtois-Rivet, P.-M.; and Michaud, F. 2020 · 2020
Later among the works it cites.
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Unflow: Unsupervised learning of optical flow with a bidirectional census loss
Meister, S.; Hur, J.; and Roth, S. 2018 · 2018
Cited alongside, same era.
Online temporal calibration for monocular visual-inertial systems
Qin, T.; and Shen, S. 2018 · 2018
Cited alongside, same era.
The TUM VI benchmark for evaluating visual-inertial odometry
Schubert, D.; Goll, T.; Demmel, N.; Usenko, V.; Stückler, J.; and Cremers, D. 2018 · 2018
Cited alongside, same era.
Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume
Sun, D.; Yang, X.; Liu, M.-Y.; and Kautz, J. 2018 · 2018
Cited alongside, same era.
Deepv2d: Video to depth with differentiable structure from motion
Teed, Z.; and Deng, J. 2018 · 2018
Cited alongside, same era.
End-to-end, sequence-to-sequence probabilistic visual odometry through deep neural networks
Wang, S.; Clark, R.; Wen, H.; and Trigoni, N. 2018 · 2018
Cited alongside, same era.
TartanAir: A Dataset to Push the Limits of Visual SLAM
Wang, W.; Zhu, D.; Wang, X.; Hu, Y.; Qiu, Y.; Wang, C.; Hu, Y.; Kapoor, A.; and Scherer, S. 2020 · 2020
Later among the works it cites.
D3vo: Deep depth, deep pose and deep uncertainty for monocular visual odometry
Yang, N.; Stumberg, L. v.; Wang, R.; and Cremers, D. 2020 · 2020
Later among the works it cites.
Direct sparse mapping
Zubizarreta, J.; Aguinaga, I.; and Montiel, J. M. M. 2020 · 2020
Later among the works it cites.
Deep vit features as dense visual descriptors
Amir, S.; Gandelsman, Y.; Bagon, S.; and Dekel, T. 2021 · 2021
Later among the works it cites.
SLIM: Self-Supervised LiDAR Scene Flow and Motion Segmentation
Baur, S.; Emmerichs, D.; Moosmann, F.; Pinggera, P.; Ommer, B.; and Geiger, A. 2021 · 2021
Later among the works it cites.
Emerging properties in self-supervised vision transformers
Caron, M.; Touvron, H.; Misra, I.; Jégou, H.; Mairal, J.; Bojanowski, P.; and Joulin, A. 2021 · 2021
Later among the works it cites.
EffiScene: Efficient Per-Pixel Rigidity Inference for Unsupervised Joint Learning of Optical Flow, Depth, Camera Pose and Motion Segmentation
Jiao, Y.; Tran, T. D.; and Shi, G. 2021 · 2021
Later among the works it cites.
Upflow: Upsampling pyramid for unsupervised optical flow learning
Luo, K.; Wang, C.; Liu, S.; Fan, H.; Wang, J.; and Sun, J. 2021 · 2021
Later among the works it cites.
DymSLAM: 4D Dynamic Scene Reconstruction Based on Geometrical Motion Segmentation
Wang, C.; Luo, B.; Zhang, Y.; Zhao, Q.; Yin, L.; Wang, W.; Su, X.; Wang, Y.; and Li, C. 2021 · 2021
Later among the works it cites.
Tartanvo: A generalizable learning-based vo
Wang, W.; Hu, Y.; and Scherer, S. 2021 · 2021
Later among the works it cites.
SuperPlane: 3D Plane Detection and Description from a Single Image
Ye, W.; Li, H.; Zhang, T.; Zhou, X.; Bao, H.; and Zhang, G. 2021 · 2021
Later among the works it cites.
DF-VO: What Should Be Learnt for Visual Odometry?
Zhan, H.; Weerasekera, C. S.; Bian, J.-W.; Garg, R.; and Reid, I. 2021 · 2021
Later among the works it cites.
Disentangling object motion and occlusion for unsupervised multi-frame monocular depth
Feng, Z.; Yang, L.; Jing, L.; Wang, H.; Tian, Y.; and Li, B. 2022 · 2022
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Masked autoencoders are scalable vision learners
He, K.; Chen, X.; Xie, S.; Li, Y.; Dollár, P.; and Girshick, R. 2022 · 2022
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DGS-SLAM: A Fast and Robust RGBD SLAM in Dynamic Environments Combined by Geometric and Semantic Information
Yan, L.; Hu, X.; Zhao, L.; Chen, Y.; Wei, P.; and Xie, H. 2022 · 2022
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Optical-Flow-Aided Self-Supervised Single View Depth Estimation from Monocular Videos
Bello, J. L. G.; and Kim, M. 2023 · 2023
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Dinov2: Learning robust visual features without supervision
Oquab, M.; Darcet, T.; Moutakanni, T.; Vo, H.; Szafraniec, M.; Khalidov, V.; Fernandez, P.; Haziza, D.; Massa, F.; El-Nouby, A.; et al. 2023 · 2023
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PVO: Panoptic visual odometry
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Pointodyssey: A large-scale synthetic dataset for long-term point tracking
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