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Awesome-object-pose-estimation

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## Awesome-object-pose

This repository is a list of papers and open source code for 6D Object Pose Estimation.

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### Vision Based

- Multimodal Templates for Real-Time Detection of Texture-less Objects in Heavily Cluttered Scenes - 2011 [[paper]](http://campar.in.tum.de/pub/hinterstoisser2011linemod/hinterstoisser2011linemod.pdf) [[code]](http://campar.in.tum.de/pub/hinterstoisser2011linemod/hinterstoisser2011linemod.pdf)
  - Uncertainty-Driven 6D Pose Estimation of Objects and Scenes from a Single RGB Image - 2016 IEEE [[paper]](http://wwwpub.zih.tu-dresden.de/~cvweb/publications/papers/2016/rgbpose.pdf)
### Deep learning Based

- bb8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using Depth - 2017 ICCV [[paper]](https://arxiv.org/abs/1703.10896)[[ppt]](https://github.com/MyungHaSong/Awesome-object-pose-estimation/blob/master/bb8.pdf)
 - iPose: Instance-Aware 6D Pose Estimation of Partly Occluded Objects - 2017 [[paper]](https://arxiv.org/abs/1712.01924)
 
  - PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes - 2017 [[paper]](https://arxiv.org/abs/1711.00199)[[code]](https://github.com/yuxng/PoseCNN)
   
  - Real-Time Seamless Single Shot 6D Object Pose Prediction - 2018 [[paper]](https://arxiv.org/abs/1711.08848)[[code]](https://github.com/Microsoft/singleshotpose)
  
  - Deep Object Pose Estimation for Segmantic Robotic Grasping of Household Objects-2018 [[paper]](https://arxiv.org/abs/1809.10790)[[code]](https://github.com/NVlabs/Deep_Object_Pose)

- Deep-6DPose: Recovering 6D Object Pose from a Single RGB Image - 2018 [[paper]](https://arxiv.org/abs/1802.10367)
  - DeepIM: Deep Iterative Matching for 6D Pose Estimation - 2018  [[paper]](https://arxiv.org/abs/1804.00175)[[code]](https://github.com/liyi14/mx-DeepIM)
  - PVNet: Pixel-wise Voting Network for 6DoF Pose Estimation -2018 [[paper]](https://arxiv.org/pdf/1812.11788.pdf)[[code]](https://github.com/zju3dv/pvnet)

- 6D Object Pose Estimation Based on 2D Bounding Box - 2019 WSPC [[paper]](https://arxiv.org/abs/1901.09366)
  
  - 6D Object Pose Estimation without PnP -2019 [[paper]](https://arxiv.org/abs/1902.01728)
  
  - Implicit 3D Orientation Learning for 6D Object Detection from RGB Images - 2018 ECCV [[paper]](https://arxiv.org/abs/1902.01275)
  
  - Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation -2019 CVPR [[paper]](https://arxiv.org/abs/1901.02970)[[code]](https://github.com/hughw19/NOCS_CVPR2019)

- DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion -2019 CVPR [[paper]](https://arxiv.org/abs/1901.04780)[[code]](https://github.com/j96w/DenseFusion)[[Code Review]](https://github.com/MyungHaSong/DenseFusion-Code-Review)
 
  - Segmentation-driven 6D Object Pose Estimation - 2019 CVPR [[paper]](https://arxiv.org/pdf/1812.02541.pdf)[[code]](https://github.com/cvlab-epfl/segmentation-driven-pose)

- DPOD: 6D Pose Object Detector and Refiner -2019 ICCV [[paper]](https://arxiv.org/pdf/1902.11020.pdf)
  
  - Explaining the Ambiguity of Object Detection and 6D Pose From Visual Data -2019 ICCV[[paper]](https://arxiv.org/abs/1812.00287)
  
  - W-PoseNet: Dense Correspondence Regularized Pixel Pair Pose Regression - 2019 CoRL[[paper]](https://arxiv.org/pdf/1912.11888.pdf)
  
  - 6D Pose Estimation with Correlation Fusion - 2019 arXiv.org [[paper]](https://arxiv.org/abs/1909.12936)
  
  - PVN3D: A Deep Point-wise 3D Keypoints Voting Network for 6DoF Pose Estimation -2020 CVPR [[paper]](https://arxiv.org/abs/1911.04231)[[code]](https://github.com/ethnhe/PVN3D)
  - PFRL: Pose-Free Reinforcement Learning for 6D Pose Estimation - 2020 CVPR [[paper]](https://openaccess.thecvf.com/content_CVPR_2020/papers/Shao_PFRL_Pose-Free_Reinforcement_Learning_for_6D_Pose_Estimation_CVPR_2020_paper.pdf)
  - EPOS: Estimating 6D Pose of Objects with Symmetries - 2020 CVPR [[paper]](https://openaccess.thecvf.com/content_CVPR_2020/papers/Hodan_EPOS_Estimating_6D_Pose_of_Objects_With_Symmetries_CVPR_2020_paper.pdf)
  - Learning Canonical Shape Space for Category-Level 6D Object Pose and Size Estimation -2020 CVPR [[paper]](https://openaccess.thecvf.com/content_CVPR_2020/papers/Chen_Learning_Canonical_Shape_Space_for_Category-Level_6D_Object_Pose_and_CVPR_2020_paper.pdf)
  - MoreFusion: Multi-object Reasoning for 6D Pose Estimation from Volumetric Fusion - 2020 CVPR[[paper]](https://openaccess.thecvf.com/content_CVPR_2020/papers/Wada_MoreFusion_Multi-object_Reasoning_for_6D_Pose_Estimation_from_Volumetric_Fusion_CVPR_2020_paper.pdf)
  - Single-Stage 6D Object Pose Estimation -2020 CVPR[[paper]](https://openaccess.thecvf.com/content_CVPR_2020/papers/Hu_Single-Stage_6D_Object_Pose_Estimation_CVPR_2020_paper.pdf)
  - G2L-Net: Global to Local Network for Real-time 6D Pose Estimation with Embedding Vector Features 2020 CVPR [[paper]](https://openaccess.thecvf.com/content_CVPR_2020/papers/Chen_G2L-Net_Global_to_Local_Network_for_Real-Time_6D_Pose_Estimation_CVPR_2020_paper.pdf)
  - HybridPose: 6D Object Pose Estimation under Hybrid Representations[[paper]](https://openaccess.thecvf.com/content_CVPR_2020/papers/Song_HybridPose_6D_Object_Pose_Estimation_Under_Hybrid_Representations_CVPR_2020_paper.pdf)
  - Multi-Path Learning for Object Pose Estimation Across Domains[[paper]](https://openaccess.thecvf.com/content_CVPR_2020/html/Sundermeyer_Multi-Path_Learning_for_Object_Pose_Estimation_Across_Domains_CVPR_2020_paper.html) - 2020CVPR
  - se(3)-TrackNet: Data-driven 6D Pose Tracking by Calibrating Image Residuals in Synthetic Domains 2020 IROS [[paper]](https://arxiv.org/abs/2007.13866)
  - PERCH 2.0 : Fast and Accurate GPU-based Perception via Search for Object Pose Estimation [[paper]](https://arxiv.org/abs/2008.00326)
  - CosyPose: Consistent multi-view multi-object 6D pose estimation 	ECCV 2020[[paper]](https://arxiv.org/abs/2008.08465)

---
## Database

- LINEMOD[[link]](http://campar.in.tum.de/Main/StefanHinterstoisser)
- OccludedLINEMOD[[link]](https://hci.iwr.uni-heidelberg.de/vislearn/iccv2015-occlusion-challenge/)
- T-Less[[link]](http://cmp.felk.cvut.cz/t-less/)
- YCB Video Dataset[[link]](http://www.ycbbenchmarks.com/)

Awesome-object-pose-estimation

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