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# awesome-object-pose
This repository is a curated list of papers and open source code for 6D Object Pose Estimation.

## Papers
* Segmentation-driven 6D Object Pose Estimation - Yinlin Hu, Joachim Hugonot, Pascal Fua, Mathieu Salzmann.[[Paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Hu_Segmentation-Driven_6D_Object_Pose_Estimation_CVPR_2019_paper.pdf)
* HybridPose: 6D Object Pose Estimation under Hybrid Representations - Chen Song, Jiaru Song, Qixing Huang. [[Paper]](https://arxiv.org/pdf/2001.01869.pdf)
* Single-Stage 6D Object Pose Estimation - Yinlin Hu,Pascal Fua,Wei Wang,Mathieu Salzmann. [[Paper]](https://arxiv.org/pdf/1911.08324.pdf)
* SilhoNet: An RGB Method for 6D Object Pose Estimation - Gideon Billings, Matthew Johnson-Roberson. [[Paper]](https://arxiv.org/pdf/1809.06893.pdf)
* PVNet: Pixel-wise Voting Network for 6DoF Pose Estimation -  Sida Peng, Yuan Liu, Qixing Huang, Xiaowei Zhou, Hujun Bao. [[Paper]](https://arxiv.org/pdf/1812.11788.pdf)
* Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation -  He Wang, Srinath Sridhar, Jingwei Huang, Julien Valentin, Shuran Song, Leonidas J. Guibas. [[Paper]](https://arxiv.org/pdf/1901.02970v1.pdf)
* DPOD: 6D Pose Object Detector and Refiner - Sergey Zakharov, Ivan Shugurov, Slobodan Ilic. [[Paper]](https://arxiv.org/pdf/1902.11020v2.pdf)
* Instance- and Category-level 6D Object Pose Estimation - Caner Sahin, Guillermo Garcia-Hernando, Juil Sock, Tae-Kyun Kim. [[Paper]](https://arxiv.org/pdf/1903.04229v1.pdf)
* Vision-based Robotic Grasping from Object Localization, Pose Estimation, Grasp Detection to Motion Planning: A Review - Guoguang Du, Kai Wang, Shiguo Lian. [[Paper]](https://arxiv.org/pdf/1905.06658v1.pdf)
* HomebrewedDB: RGB-D Dataset for 6D Pose Estimation of 3D Objects - Roman Kaskman, Sergey Zakharov, Ivan Shugurov, Slobodan Ilic. [[Paper]](https://arxiv.org/pdf/1904.03167v1.pdf)
* Summary of the 4th International Workshopon Recovering 6D Object Pose - Tomas Hodan, Rigas Kouskouridas, Tae-Kyun Kim, Federico Tombari, Kostas Bekris, Bertram Drost, Thibault Groueix, Krzysztof Walas, Vincent Lepetit, Ales Leonardis, Carsten Steger, Frank Michel, Caner Sahin, Carsten Rother, Jirı Matas. [[Paper]](http://openaccess.thecvf.com/content_ECCVW_2018/papers/11129/Hodan_A_Summary_of_the_4th_International_Workshop_onRecovering_6D_Object_ECCVW_2018_paper.pdf)
* DeepIM: Deep Iterative Matching for 6D Pose Estimation - Yi Li, Gu Wang, Xiangyang Ji, Yu Xiang, Dieter Fox. [[Paper]](https://arxiv.org/pdf/1804.00175.pdf)
* Robust 6D Object Pose Estimation in Cluttered Scenesusing Semantic Segmentation and Pose Regression Networks - Arul Selvam Periyasamy, Max Schwarz, and Sven Behnke. [[Paper]](https://www.ais.uni-bonn.de/papers/IROS_2018_Periyasamy.pdf)
* Category-level 6D Object Pose Recovery in Depth Images - Caner Sahin and Tae-Kyun Kim. [[Paper]](http://openaccess.thecvf.com/content_ECCVW_2018/papers/11129/Sahin_Category-level_6D_Object_Pose_Recovery_in_Depth_Images_ECCVW_2018_paper.pdf)
* Matching RGB Images to CAD Models for Object Pose Estimation - Georgios Georgakis, Srikrishna Karanam, Ziyan Wu, and Jana Kosecka. [[Paper]](https://arxiv.org/pdf/1811.07249.pdf)
* Implicit 3D Orientation Learning for 6D Object Detection from RGB Images - Martin Sundermeyer, Zoltan-Csaba Marton, Maxmilian Durner, Manuel Brucker and Rudolph Triebel. [[Paper]](https://arxiv.org/pdf/1902.01275v1.pdf)
* DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion - Chen Wang, Danfei Xu, Yuke Zhu, Roberto Martín-Martín, Cewu Lu, Li Fei-Fei, Silvio Savarese. [[Paper]](https://arxiv.org/pdf/1901.04780.pdf)
* Real-Time 6D Object Pose Estimation on CPU - Yoshinori Konishi, Kosuke Hattori, Manabu Hashimoto. [[Paper]](https://arxiv.org/pdf/1811.08588.pdf)
* Holistic and local patch framework for 6D object pose estimation in RGB-D images - Haoruo Zhang, Qixin Cao. [[Paper]](https://www.sciencedirect.com/science/article/pii/S1077314219300050)
* Estimating 6D Pose From Localizing Designated Surface Keypoints - Zelin Zhao, Gao Peng, Haoyu Wang, Hao-Shu Fang, Chengkun Li, Cewu Lu. [[Paper]](https://arxiv.org/pdf/1812.01387v1.pdf)
* Real-Time Object Pose Estimation with Pose Interpreter Networks- Jimmy Wu, Bolei Zhou, Rebecca Russell, Vincent Kee, Syler Wagner, Mitchell Hebert, Antonio Torralba, David M.S. Johnson. [[Paper]](https://arxiv.org/pdf/1808.01099.pdf)
* Segmentation-driven 6D Object Pose Estimation - Yinlin Hu, Joachim Hugonot, Pascal Fua, Mathieu Salzmann. [[Paper]](https://arxiv.org/pdf/1812.02541.pdf)
* Robust 6D Object Pose Estimation with Stochastic Congruent Sets - Chaitanya Mitash, Abdeslam Boularias, Kostas E. Bekris. [[Paper]](http://bmvc2018.org/contents/papers/1046.pdf)
* BOP: Benchmark for 6D Object Pose Estimation - Tomas Hodan, Frank Michel, Eric Brachmann, Wadim Kehl, Anders Glent Buch, Dirk Kraft, Bertram Drost, Joel Vidal, Stephan Ihrke, Xenophon Zabulis, Caner Sahin, Fabian Manhardt, Federico Tombari, Tae-Kyun Kim, Jiri Matas, Carsten Rother. [[Paper]](https://arxiv.org/pdf/1808.08319.pdf)
* Deep Object Pose Estimation for Semantic Robotic Grasping of Household Objects - Jonathan Tremblay, Thang To, Balakumar Sundaralingam, Yu Xiang, Dieter Fox, Stan Birchfield. [[Paper]](https://arxiv.org/pdf/1809.10790.pdf)
* PoseCNN: Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes - Yu Xiang, Tanner Schmidt, Venkatraman Narayanan and Dieter Fox. [[Paper]](https://arxiv.org/pdf/1711.00199.pdf)
* Multi-view 6D Object Pose Estimation and Camera Motion Planning Using RGBD Images - Juil Sock, S. Hamidreza Kasaei, Luís Seabra Lopes, Tae-Kyun Kim. [[Paper]](https://ieeexplore.ieee.org/document/8265470)
* Global Hypothesis Generation for 6D Object Pose Estimation - Frank Michel, Alexander Kirillov,Eric Brachmann, Alexander Krull, Stefan Gumhold, Bogdan Savchynskyy, Carsten Rother. [[Paper]](https://ieeexplore.ieee.org/document/8099503) 
* BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using Depth - Mahdi Rad, Vincent Lepetit. [[Paper]](https://arxiv.org/abs/1703.10896)
* Real-Time Seamless Single Shot 6D Object Pose Prediction - Bugra Tekin, Sudipta N. Sinha, Pascal Fua. [[Paper]](https://arxiv.org/pdf/1711.08848.pdf)
* SSD-6D: Making RGB-based 3D detection and 6D pose estimation great again - Wadim Kehl, Fabian Manhardt, Federico Tombari, Slobodan Ilic, Nassir Navab. [[Paper]](https://arxiv.org/pdf/1711.10006.pdf)
* Deep Learning of Local RGB-D Patches for 3D Object Detection and 6D Pose Estimation - Wadim Kehl, Fausto Milletari, Federico Tombari, Slobodan Ilic, Nassir Navab. [[Paper]](https://arxiv.org/pdf/1607.06038.pdf)
* Deep-6DPose: Recovering 6D Object Pose from a Single RGB Image - Thanh-Toan Do, Ming Cai, Trung Pham, Ian Reid. [[Paper]] (https://arxiv.org/pdf/1802.10367.pdf)
* Learning 6D Object Pose Estimation Using 3D Object Coordinates - Eric Brachmann, Alexander Krull, Frank Michel, Stefan Gumhold, Jamie Shotton, Carsten Rother. [[Paper]](https://link.springer.com/content/pdf/10.1007%2F978-3-319-10605-2_35.pdf)
* The MOPED framework: Object recognition and pose estimation for manipulation - Alvaro Collet Romea, Manuel Martinez Torres and Siddhartha Srinivasa. [[Paper]](https://www.ri.cmu.edu/pub_files/2011/9/moped.pdf)



## Code
* [HybridPose](https://github.com/chensong1995/HybridPose)
* [PoseCNN](https://github.com/yuxng/PoseCNN)
* [Single Shot Pose Estimation](https://github.com/Microsoft/singleshotpose)
* [SSD-6D](https://github.com/wadimkehl/ssd-6d)
* [Dope Object Pose Estimation](https://github.com/NVlabs/Deep_Object_Pose)
* [Pose Interpreter Networks](https://github.com/jimmyyhwu/pose-interpreter-networks)
* [Tools for Evaluation of 6D Object Pose Estimation](https://github.com/thodan/obj_pose_eval)
* [Augmented Autoencoder](https://github.com/DLR-RM/AugmentedAutoencoder)
* [DeepIM](https://github.com/liyi14/mx-DeepIM)
* [DenseFusion](https://github.com/j96w/DenseFusion)
* [BetaPose](https://github.com/sjtuytc/betapose)
* [PVNet](https://github.com/zju3dv/pvnet)

## Datasets
* LINEMOD
* OccludedLINEMOD
* YCB Video Dataset [[Dataset]](https://rse-lab.cs.washington.edu/projects/posecnn/) [[Code]](https://github.com/yuxng/YCB_Video_toolbox)
* Falling Things [[Dataset]](http://research.nvidia.com/publication/2018-06_Falling-Things) [[Paper]](https://arxiv.org/pdf/1804.06534.pdf) 
* Rutgers 6D Object Pose Estimation [[Dataset]](http://www.pracsyslab.org/pose_estimation)

## Tutorials
* Real-time pose estimation of a textured object[[Link]](https://docs.opencv.org/3.3.0/dc/d2c/tutorial_real_time_pose.html)
* Pose estimation from points[[Link]](http://visp-doc.inria.fr/doxygen/visp-daily/tutorial-pose-estimation.html)