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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Awesome work on object 6 DoF pose estimation
| Date | Stars |
|---|---|
| 2026-07-24 | 887 |
| 2026-07-25 | 887 |
| 2026-07-28 | 887 |
| 2026-07-30 | 887 |
| 2026-08-06 | 887 |
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growth rate 0.00%/day
# Awesome Object Pose Estimation and Reconstruction [](https://awesome.re)
A curated list of related resources for 6D object pose estimation, also including 3D objects reconstruction from a single view, and 3D hand-object pose estimation. 🔥 means new update.
Due to my personal interests, geometry-based work (SFM-based or SLAM-based work) are not collected here. Those papers can be found [here](https://github.com/openMVG/awesome_3DReconstruction_list).
Another related paper list is about the hand pose estimation, which can be found [here](https://github.com/xinghaochen/awesome-hand-pose-estimation).
Some awesome open-source demos ([CenterSnap](https://github.com/zubair-irshad/CenterSnap), [NOCS](https://hughw19.github.io/NOCS_CVPR2019), [BundleTrack](https://github.com/wenbowen123/BundleTrack) and [se(3)-TrackNet](https://github.com/wenbowen123/iros20-6d-pose-tracking))：
<p float="left">
<img src="./media/centersnap_reconstruction.gif" width="600" />
</p>
<p float="left">
<img src="./media/6dtracking.gif" width="300" />
<img src="./media/ycbineoat.gif" width="300" />
</p>
<p float="left">
<img src="./media/ycb_packing.gif" width="600" />
</p>
## Contents
<!-- - [Evaluation](#evaluation) -->
- [arXiv Papers🔥](#arxiv-papers)
- [Journal Papers](#journal-papers)
- [TPAMI / IJCV](#tpami--ijcv)
- [Others](#other-journals)
- [Conference Papers](#conference-papers)
- 2024: [CVPR🔥](#2024-cvpr)
- 2023: [CVPR](#2023-cvpr), [ICCV](#2023-iccv), [ICRA](#2023-icra), [IROS](#2023-iros)
- 2022: [CVPR](#2022-cvpr), [ECCV](#2022-eccv), [ICRA](#2022-icra), [IROS](#2022-iros), [Others](#2022-others)
- 2021: [CVPR](#2021-cvpr), [ICCV](#2021-iccv), [ICRA](#2021-icra), [IROS](#2021-iros), [Others](#2021-others)
- 2020: [CVPR](#2020-cvpr), [ECCV](#2020-eccv), [Others](#2020-others)
- 2019: [CVPR](#2019-cvpr), [ICCV](#2019-iccv), [Others](#2019-others)
- 2018: [CVPR](#2018-cvpr), [ECCV](#2018-eccv), [Others](#2018-others)
- 2017: [CVPR](#2017-cvpr), [ICCV](#2017-iccv), [Others](#2017-others)
- 2016: [CVPR](#2016-cvpr), [Others](#2016-others)
- 2015: [CVPR](#2015-cvpr), [ICCV](#2015-iccv), [Others](#2015-others)
- 2014: [CVPR](#2014-cvpr), [Others](#2014-others-&-before)
- [Thesis](#thesis)
- [Datasets](#datasets)
- [Benchmark 6D Object Pose Estimation](#Benchmark-6D-Object-Pose-Estimation)
- [Workshops](#workshops)
- [Workshops on 3D Vision and Robotics](#Workshops-on-3D-Vision-and-Robotics)
- 3DV&R 2021: Workshop Papers
- [Workshops on Recovering 6D Object Pose](#Workshops-on-Recovering-6D-Object-Pose)
- R6D 2020: Talk Slides, Workshop Papers
- R6D 2019: Talk Slides, Workshop Papers
- R6D 2018: Talk Slides, Workshop Papers
- [Workshops on Learning 3D Representations for Shape and Appearance](#Workshops-on-Learning-3D-Representations-for-Shape-and-Appearance)
- 3DReps 2020: Workshop Videos, Workshop Papers
- [Challenges](#challenges)
- [BOP Challenge 2020](#BOP-Challenge-2020)
- Datasets, Documents, Slides
- [BOP Challenge 2019](#BOP-Challenge-2019)
- Documents
- [Researchers](#Researchers)
- [U.S./Canada](#U.S./Canada)
- [Europe](#Europe)
- [Asia](#Asia)
- [Australia](#Australia)
\* indicates equal contribution
<!-- ## Evaluation
See folder [``evaluation``](./evaluation) to get more details about performance evaluation for hand pose estimation. -->
## arXiv Papers
##### • [\[arXiv:2303.06753\]](https://arxiv.org/abs/2303.06753) Module-Wise Network Quantization for 6D Object Pose Estimation. [\[PDF\]](https://arxiv.org/pdf/2303.06753)
##### • [\[arXiv:2303.11516\]](https://arxiv.org/abs/2303.11516) Linear-Covariance Loss for End-to-End Learning of 6D Pose Estimation. [\[PDF\]](https://arxiv.org/pdf/2303.11516)
##### • [\[arXiv:2303.13479\]](https://arxiv.org/abs/2303.13479) Prior-free Category-level Pose Estimation with Implicit Space Transformation. [\[PDF\]](https://arxiv.org/pExcerpt of 166,993 characters
Read on GitHub
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Zubair Irshad · @GeorgiaTech @TRI-ML @GT-RIPL · United States
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Zelin Zhao · United States
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Gu Wang · China
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:ed1c94be11fb5a8f, topic:computer-vision, topic:pose-estimation, desc:pose estimation