https://github.com/KwanWaiPang/Awesome-Learning-based-VO-VIO
Paper Survey for Deep Visual (-Inertial) Odometry
https://github.com/KwanWaiPang/Awesome-Learning-based-VO-VIO
List: Awesome-Learning-based-VO-VIO
awesome deep-learning slam vio visual-odometry
Last synced: 25 days ago
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Paper Survey for Deep Visual (-Inertial) Odometry
- Host: GitHub
- URL: https://github.com/KwanWaiPang/Awesome-Learning-based-VO-VIO
- Owner: KwanWaiPang
- Created: 2025-03-13T11:25:50.000Z (about 1 month ago)
- Default Branch: main
- Last Pushed: 2025-03-22T09:09:36.000Z (28 days ago)
- Last Synced: 2025-03-22T10:20:19.000Z (28 days ago)
- Topics: awesome, deep-learning, slam, vio, visual-odometry
- Homepage:
- Size: 1.43 MB
- Stars: 5
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- Awesome-Diffusion-based-SLAM - Paper List
- Awesome-Transformer-based-SLAM - Awesome-Learning-based-VO-VIO
- ultimate-awesome - Awesome-Learning-based-VO-VIO - Paper Survey for Deep Visual (-Inertial) Odometry. (Other Lists / Julia Lists)
README
Awesome Learning-based VO, VIO, and IO
This repository contains a curated list of resources addressing Learning-based visual odometry, visual-inertial odometry, and inertial odometry
If you find some ignored papers, **feel free to [*create pull requests*](https://github.com/KwanWaiPang/Awesome-Transformer-based-SLAM/blob/pdf/How-to-PR.md), or [*open issues*](https://github.com/KwanWaiPang/Awesome-Learning-based-VO-VIO/issues/new)**.
Contributions in any form to make this list more comprehensive are welcome.
If you find this repositorie is useful, a simple star should be the best affirmation. 😊
Feel free to share this list with others!
# Overview
- [Learning-based VO](#Learning-based-VO)
- [Learning-based VIO](#Learning-based-VIO)
- [Learning-based IO](#Learning-based-IO)
## Learning-based VO
| Year | Venue | Paper Title | Repository | Note |
|:----:|:-----:| ----------- |:----------:|:----:|
|2024|`CVPR`|[Leap-vo: Long-term effective any point tracking for visual odometry](https://openaccess.thecvf.com/content/CVPR2024/papers/Chen_LEAP-VO_Long-term_Effective_Any_Point_Tracking_for_Visual_Odometry_CVPR_2024_paper.pdf)|[](https://github.com/chiaki530/leapvo)|[website](https://chiaki530.github.io/projects/leapvo/)|
|2024|`RAL`|[Efficient Camera Exposure Control for Visual Odometry via Deep Reinforcement Learning](https://arxiv.org/pdf/2408.17005)|[](https://github.com/ShuyangUni/drl_exposure_ctrl)|---|
|2024|`ECCV`|[Reinforcement learning meets visual odometry](https://arxiv.org/pdf/2407.15626)|[](https://github.com/uzh-rpg/rl_vo)|[Blog](https://kwanwaipang.github.io/RL-for-VO/)|
|2024|`IROS`|[Deep Visual Odometry with Events and Frames](https://arxiv.org/pdf/2309.09947)|[](https://github.com/uzh-rpg/rampvo)|RAMP-VO|
|2024|`3DV`|[Deep event visual odometry](https://arxiv.org/pdf/2312.09800)|[](https://github.com/tum-vision/DEVO)|---|
|2024|`CVPR`|[Multi-Session SLAM with Differentiable Wide-Baseline Pose Optimization](https://arxiv.org/pdf/2404.15263)|[](https://github.com/princeton-vl/MultiSlam_DiffPose)|Multi VO|
|2024|`ECCV`|[Deep patch visual slam](https://arxiv.org/pdf/2408.01654)|[](https://github.com/princeton-vl/DPVO)|---|
|2024|`NIPS`|[Deep patch visual odometry](https://proceedings.neurips.cc/paper_files/paper/2023/file/7ac484b0f1a1719ad5be9aa8c8455fbb-Paper-Conference.pdf)|[](https://github.com/princeton-vl/DPVO)|---|
|2023|`ICRA`|[Dytanvo: Joint refinement of visual odometry and motion segmentation in dynamic environments](https://arxiv.org/pdf/2209.08430)|[](https://github.com/castacks/DytanVO)|---|
|2022|`Sensor`|[Raum-vo: Rotational adjusted unsupervised monocular visual odometry](https://www.mdpi.com/1424-8220/22/7/2651)|---|---|
|2022|`Neurocomputing`|[DeepAVO: Efficient pose refining with feature distilling for deep Visual Odometry](https://arxiv.org/pdf/2105.09899)|---|---|
|2021|`CoRL`|[Tartanvo: A generalizable learning-based vo](https://proceedings.mlr.press/v155/wang21h/wang21h.pdf)|[](https://github.com/castacks/tartanvo)|---|
|2021|`NIPS`|[DROID-SLAM: Deep Visual SLAM for Monocular,Stereo, and RGB-D Cameras](https://proceedings.neurips.cc/paper/2021/file/89fcd07f20b6785b92134bd6c1d0fa42-Paper.pdf)|[](https://github.com/princeton-vl/DROID-SLAM)|---|
|2021|`CVPR`|[Generalizing to the open world: Deep visual odometry with online adaptation](https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Generalizing_to_the_Open_World_Deep_Visual_Odometry_With_Online_CVPR_2021_paper.pdf)|---|---|
|2021|`arXiv`|[DF-VO: What should be learnt for visual odometry?](https://arxiv.org/pdf/2103.00933)|[](https://github.com/Huangying-Zhan/DF-VO)|---|
|2020|`ICRA`|[Visual odometry revisited: What should be learnt?](https://arxiv.org/pdf/1909.09803)|[](https://github.com/Huangying-Zhan/DF-VO)|---|
|2020|`CVPR`|[Towards better generalization: Joint depth-pose learning without posenet](https://openaccess.thecvf.com/content_CVPR_2020/papers/Zhao_Towards_Better_Generalization_Joint_Depth-Pose_Learning_Without_PoseNet_CVPR_2020_paper.pdf)|[](https://github.com/B1ueber2y/TrianFlow)|---|
|2020|`CVPR`|[Diffposenet: Direct differentiable camera pose estimation](https://openaccess.thecvf.com/content/CVPR2022/papers/Parameshwara_DiffPoseNet_Direct_Differentiable_Camera_Pose_Estimation_CVPR_2022_paper.pdf)|---|---|
|2020|`CVPR`|[D3vo: Deep depth, deep pose and deep uncertainty for monocular visual odometry](https://openaccess.thecvf.com/content_CVPR_2020/papers/Yang_D3VO_Deep_Depth_Deep_Pose_and_Deep_Uncertainty_for_Monocular_CVPR_2020_paper.pdf)|---|[website](https://cvg.cit.tum.de/research/vslam/d3vo)|
|2021|`IJCV`|[Unsupervised scale-consistent depth learning from video](https://arxiv.org/pdf/2105.11610)|[](https://github.com/JiawangBian/SC-SfMLearner-Release)
[](https://github.com/JiawangBian/sc_depth_pl)|---|
|2019|`NIPS`|[Unsupervised scale-consistent depth and ego-motion learning from monocular video](https://proceedings.neurips.cc/paper/2019/file/6364d3f0f495b6ab9dcf8d3b5c6e0b01-Paper.pdf)|[](https://github.com/JiawangBian/SC-SfMLearner-Release)
[](https://github.com/JiawangBian/sc_depth_pl)|---|
|2019|`ICRA`|[Ganvo: Unsupervised deep monocular visual odometry and depth estimation with generative adversarial networks](https://arxiv.org/pdf/1809.05786)|---|---|
|2019|`CVPR`|[Recurrent neural network for (un-) supervised learning of monocular video visual odometry and depth](https://openaccess.thecvf.com/content_CVPR_2019/papers/Wang_Recurrent_Neural_Network_for_Un-Supervised_Learning_of_Monocular_Video_Visual_CVPR_2019_paper.pdf)|-|-|
|2019|`ICRA`|[Pose graph optimization for unsupervised monocular visual odometry](https://arxiv.org/pdf/1903.06315)|---|---|
|2018|`CVPR`|[Unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction](https://openaccess.thecvf.com/content_cvpr_2018/papers/Zhan_Unsupervised_Learning_of_CVPR_2018_paper.pdf)|[](https://github.com/Huangying-Zhan/Depth-VO-Feat)|---|
|2018|`ICRA`|[Undeepvo: Monocular visual odometry through unsupervised deep learning](https://arxiv.org/pdf/1709.06841)|-|[website](https://senwang.gitlab.io/UnDeepVO/)|
|2017|`ICRA`|[Deepvo: Towards end-to-end visual odometry with deep recurrent convolutional neural networks](https://arxiv.org/pdf/1709.08429)|-|[website](https://senwang.gitlab.io/DeepVO/)|
|2017|`CVPR`|[Unsupervised learning of depth and ego-motion from video](https://openaccess.thecvf.com/content_cvpr_2017/papers/Zhou_Unsupervised_Learning_of_CVPR_2017_paper.pdf)|[](https://github.com/tinghuiz/SfMLearner)|---|
## Learning-based VIO
| Year | Venue | Paper Title | Repository | Note |
|:----:|:-----:| ----------- |:----------:|:----:|
|2025|`Robotics and Autonomous Systems`|[CUAHN-VIO: Content-and-uncertainty-aware homography network for visual-inertial odometry](https://www.sciencedirect.com/science/article/pii/S0921889024002501)|[](https://github.com/tudelft/CUAHN-VIO)|---|
|2025|`TRO`|[Airslam: An efficient and illumination-robust point-line visual slam system](https://arxiv.org/pdf/2408.03520)| [](https://github.com/sair-lab/AirSLAM)|---|
|2024|`arXiv`|[SF-Loc: A Visual Mapping and Geo-Localization System based on Sparse Visual Structure Frames](https://arxiv.org/pdf/2412.01500)|[](https://github.com/GREAT-WHU/SF-Loc)|[test](https://kwanwaipang.github.io/SF-Loc/)|
|2024|`arXiv`|[DEIO: Deep Event Inertial Odometry](https://arxiv.org/pdf/2411.03928)|[](https://github.com/arclab-hku/DEIO)|[website](https://kwanwaipang.github.io/DEIO/)|
|2024|`CVPR`|[Adaptive vio: Deep visual-inertial odometry with online continual learning](https://openaccess.thecvf.com/content/CVPR2024/papers/Pan_Adaptive_VIO_Deep_Visual-Inertial_Odometry_with_Online_Continual_Learning_CVPR_2024_paper.pdf)|---|---|
|2024|`RAL`|[DBA-Fusion: Tightly Integrating Deep Dense Visual Bundle Adjustment with Multiple Sensors for Large-Scale Localization and Mapping](https://arxiv.org/pdf/2403.13714)|[](https://github.com/GREAT-WHU/DBA-Fusion)|---|
|2024|`ICRA`|[DVI-SLAM: A dual visual inertial SLAM network](https://arxiv.org/pdf/2309.13814)|---|---|
|2023|`ICRA`|[Bamf-slam: bundle adjusted multi-fisheye visual-inertial slam using recurrent field transforms](https://arxiv.org/pdf/2306.01173)|---|---|
|2022|`ECCV`|[Efficient deep visual and inertial odometry with adaptive visual modality selection](https://arxiv.org/pdf/2205.06187)|[](https://github.com/mingyuyng/Visual-Selective-VIO)|---|
|2022|`IEEE/ASME International Conference on Advanced Intelligent Mechatronics`|[A self-supervised, differentiable Kalman filter for uncertainty-aware visual-inertial odometry](https://arxiv.org/pdf/2203.07207)|---|---|
|2022|`Neural Networks`|[SelfVIO: Self-supervised deep monocular Visual–Inertial Odometry and depth estimation](https://www.sciencedirect.com/science/article/pii/S0893608022000752)|---|---|
|2021|`International Conference on International Joint Conferences on Artificial Intelligence`|[Unsupervised monocular visual-inertial odometry network](https://www.ijcai.org/proceedings/2020/0325.pdf)|[](https://github.com/Ironbrotherstyle/UnVIO)|---|
|2021|`IEEE International Conference on Acoustics, Speech and Signal Processing `|[Atvio: Attention guided visual-inertial odometry](https://github.com/KwanWaiPang/Awesome-Learning-based-VO-VIO/blob/pdf/file/ATVIO_Attention_Guided_Visual-Inertial_Odometry.pdf)|---|---|
|2019|`TPAMI`|[Unsupervised deep visual-inertial odometry with online error correction for RGB-D imagery](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8691513)|---|---|
|2019|`CVPR`|[Selective sensor fusion for neural visual-inertial odometry](https://openaccess.thecvf.com/content_CVPR_2019/papers/Chen_Selective_Sensor_Fusion_for_Neural_Visual-Inertial_Odometry_CVPR_2019_paper.pdf)|---|---|
|2019|`IROS`|[Deepvio: Self-supervised deep learning of monocular visual inertial odometry using 3d geometric constraints](https://arxiv.org/pdf/1906.11435)|---|---|
|2017|`AAAI`|[Vinet: Visual-inertial odometry as a sequence-to-sequence learning problem](https://arxiv.org/pdf/1701.08376)|[](https://github.com/HTLife/VINet)|non-official implementation|
## Learning-based IO
| Year | Venue | Paper Title | Repository | Note |
|:----:|:-----:| ----------- |:----------:|:----:|
|2025|`arXiv`|[AirIO: Learning Inertial Odometry with Enhanced IMU Feature Observability](https://arxiv.org/pdf/2501.15659)|[](https://github.com/Air-IO/Air-IO)|[website](https://air-io.github.io/)
[Test](https://kwanwaipang.github.io/AirIO/)|
|2024|`TIV`|[Deep learning for inertial positioning: A survey](https://arxiv.org/pdf/2303.03757)|---|---|
|2023|`arXiv`|[End-to-end deep learning framework for real-time inertial attitude estimation using 6dof imu](https://www.researchgate.net/profile/Arman-Asgharpoor-Golroudbari/publication/368469434_END-TO-END_DEEP_LEARNING_FRAMEWORK_FOR_REAL-TIME_INERTIAL_ATTITUDE_ESTIMATION_USING_6DOF_IMU/links/63ea42cebd7860764364396a/End-to-End-Deep-Learning-Framework-for-Real-Time-Inertial-Attitude-Estimation-using-6DoF-IMU.pdf)|---|---|
|2023|`arXiv`|[AirIMU: Learning uncertainty propagation for inertial odometry](https://arxiv.org/pdf/2310.04874)|[](https://github.com/haleqiu/AirIMU)|[website](https://airimu.github.io/)
[Test](https://kwanwaipang.github.io/AirIMU/)|
|2023|`RAL`|[Learned inertial odometry for autonomous drone racing](https://arxiv.org/pdf/2210.15287)|[](https://github.com/uzh-rpg/learned_inertial_model_odometry)|---|
|2022|`ICRA`|[Improved state propagation through AI-based pre-processing and down-sampling of high-speed inertial data](https://www.aau.at/wp-content/uploads/2022/03/imu_preprocessing.pdf)|---|---|
|2022|`RAL`|[Deep IMU Bias Inference for Robust Visual-Inertial Odometry with Factor Graphs](https://arxiv.org/pdf/2211.04517)|---|---|
|2021|`ICRA`|[IMU Data Processing For Inertial Aided Navigation:A Recurrent Neural Network Based Approach](https://arxiv.org/pdf/2103.14286)|---|---|
## Other Related Resource
* [Awesome-Transformer-based-SLAM](https://github.com/KwanWaiPang/Awesome-Transformer-based-SLAM)
* [Blog for Learning-based VO and VIO](https://kwanwaipang.github.io/Learning-based-VO-VIO/)
* [Blog for Deep IMU-Bias Inference](https://kwanwaipang.github.io/Deep-IMU-Bias/)
* Some related papers for learning-based VO,VIO, such as dataset, depth estimation, etc.
| Year | Venue | Paper Title | Repository | Note |
|:----:|:-----:| ----------- |:----------:|:----:|
|2024|`TPAMI`|[Metric3d v2: A versatile monocular geometric foundation model for zero-shot metric depth and surface normal estimation](https://arxiv.org/pdf/2404.15506)|[](https://github.com/YvanYin/Metric3D)|[website](https://jugghm.github.io/Metric3Dv2/)
depth estimation|
|2022|`NIPS`|[Theseus: A library for differentiable nonlinear optimization](https://proceedings.neurips.cc/paper_files/paper/2022/file/185969291540b3cd86e70c51e8af5d08-Paper-Conference.pdf)|[](https://github.com/facebookresearch/theseus)|[website](https://sites.google.com/view/theseus-ai/)|
|2020|`CVPR`|[Superglue: Learning feature matching with graph neural networks](https://openaccess.thecvf.com/content_CVPR_2020/papers/Sarlin_SuperGlue_Learning_Feature_Matching_With_Graph_Neural_Networks_CVPR_2020_paper.pdf)|[](https://github.com/magicleap/SuperGluePretrainedNetwork)|---|
|2020|`IROS`|[Tartanair: A dataset to push the limits of visual slam](https://arxiv.org/pdf/2003.14338)|[](https://github.com/castacks/tartanair_tools)|[website](https://theairlab.org/tartanair-dataset/)|
|2020|`ECCV`|[Raft: Recurrent all-pairs field transforms for optical flow](https://arxiv.org/pdf/2003.12039)|[](https://github.com/princeton-vl/RAFT)|---|
|2019|`CVPR`|[Projective manifold gradient layer for deep rotation regression](https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_Projective_Manifold_Gradient_Layer_for_Deep_Rotation_Regression_CVPR_2022_paper.pdf)|[](https://github.com/JYChen18/RPMG)|Better learn rotations|
|2018|`ECCV`|[Mvsnet: Depth inference for unstructured multi-view stereo](https://openaccess.thecvf.com/content_ECCV_2018/papers/Yao_Yao_MVSNet_Depth_Inference_ECCV_2018_paper.pdf)|---|---|
|2018|`CVPR`|[Superpoint: Self-supervised interest point detection and description](https://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w9/DeTone_SuperPoint_Self-Supervised_Interest_CVPR_2018_paper.pdf)|[](https://github.com/rpautrat/SuperPoint)|[pytorch version](https://github.com/eric-yyjau/pytorch-superpoint)
[event superpoint](https://github.com/mingyip/pytorch-superpoint)|
|2015|`ICCV`|[Flownet: Learning optical flow with convolutional networks](https://openaccess.thecvf.com/content_iccv_2015/papers/Dosovitskiy_FlowNet_Learning_Optical_ICCV_2015_paper.pdf)|---|---|