单目RGB、传统方法：
（1） D. G. Lowe. Object recognition from local scale-invariant features. In ICCV, 1999.
（2） V. Lepetit, P. Fua, et al. Monocular model-based 3d tracking of rigid objects: A survey. Foundations and Trends in Computer Graphics and Vision, 1(1):1–89, 2005.
（3） S. Hinterstoisser, V. Lepetit, S. Ilic, S. Holzer, G. Bradski,K. Konolige, and N. Navab. Model based training, detection and pose
estimation of texture-less 3d objects in heavily cluttered scenes. In ACCV, 2012.
（4）M. Aubry, D. Maturana, A. A. Efros, B. C. Russell, and J.Sivic, “Seeing 3d chairs: Exemplar part-based 2d-3d alignment using a large dataset of cad models,” inProceedings of the IEEE Computer Vision and Pattern Recognition (cvpr),2014, pp. 3762–3769.
（5）A. Collet, M. Martinez, and S. S. Srinivasa, “The moped framework: Object recognition and pose estimation for manipulation,” The international journal of robotics research,vol. 30, no. 10, pp. 1284–1306, 2011.
（6） V . Ferrari, T. Tuytelaars, and L. V an Gool, “Simultaneous object recognition and segmentation from single or multiple model views,” International journal of computer vision,vol. 67, no. 2, pp. 159–188, 2006.
（7）F. Rothganger, S. Lazebnik, C. Schmid, and J. Ponce, “3d object modeling and recognition using local affine-invariant
image descriptors and multi-view spatial constraints,” International journal of computer vision, vol. 66, no. 3,pp. 231–259, 2006.
（8）M. Zhu, K. G. Derpanis, Y . Yang, S. Brahmbhatt, M.Zhang, C. Phillips, M. Lecce, and K. Daniilidis, “Single image 3d object detection and pose estimation for grasping,” in Robotics and automation (icra), 2014 ieee international conference on, IEEE, 2014, pp. 3936–3943.
单目RGB、深度学习方法：
1.直接回归法：
（1）Wadim Kehl, Fabian Manhardt, Federico Tombari, Slobodan Ilic, and Nassir Navab. SSD-6D: Making rgb-based 3D detection and 6D pose estimation great again. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
（2）Yu Xiang, Tanner Schmidt, Venkatraman Narayanan, and Dieter Fox. Posecnn: A convolutional neural network for 6d object pose estimation in cluttered scenes. In Robotics: Science and Systems (RSS), 2018.
（3） Thanh-Toan Do, Trung Pham, Ming Cai, and Ian D. Reid. Lienet: Real-time monocular object instance 6d pose estimation. In Proceedings of British Machine Vision Conference (BMVC), 2018.
2.基于稀疏关键点对应的方法：
（1） Mahdi Rad and Vincent Lepetit. BB8: A scalable, accurate, robust to partial occlusion method for predicting the 3D poses of challenging objects without using depth. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017.
（2）Georgios Pavlakos, Xiaowei Zhou, Aaron Chan, Konstantinos G Derpanis, and Kostas Daniilidis. 6-dof object pose from semantic keypoints. In Robotics and Automation(ICRA), 2017 IEEE International Conference on, 2017.
（3） Bugra Tekin, Sudipta N. Sinha, and Pascal Fua. Real-Time Seamless Single Shot 6D Object Pose Prediction. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
（4）] Xingyi Zhou, Arjun Karpur, Linjie Luo, and Qixing Huang. Starmap for category-agnostic keypoint and viewpoint estimation. In Proceedings of European Conference on Computer Vision (ECCV), 2018.
（5）Georgios Pavlakos, Xiaowei Zhou, Aaron Chan, Konstantinos G Derpanis, and Kostas Daniilidis. 6-dof object pose from semantic keypoints. In Robotics and Automation (ICRA), 2017 IEEE International Conference on, 2017.
3.基于坐标的方法（基于密集关键点对应的方法）：
（1）Wadim Kehl, Fausto Milletari, Federico Tombari, Slobodan Ilic, and Nassir Navab. Deep learning of local rgb-d patches for 3d object detection and 6d pose estimation. In Proceedings of European Conference on Computer Vision (ECCV),2016.
（2）Apurv Nigam, Adrian Penate-Sanchez, and Lourdes Agapito. Detect globally, label locally: Learning accurate 6-dof object pose estimation by joint segmentation and coordinate regression. IEEE Robotics and Automation Letters (RAL), 2018.
（3）Eric Brachmann, Frank Michel, Alexander Krull, Michael Ying Yang, Stefan Gumhold, et al. Uncertainty-driven 6d pose estimation of objects and scenes from a single rgb image. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
（4）Apurv Nigam, Adrian Penate-Sanchez, and Lourdes Agapito. Detect globally, label locally: Learning accurate 6-dof object pose estimation by joint segmentation and coordinate regression. IEEE Robotics and Automation Letters(RAL), 2018.
（5） He Wang, Srinath Sridhar, Jingwei Huang, Julien Valentin,Shuran Song, and Leonidas J Guibas. Normalized object coordinate space for category-level 6d object pose and size estimation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.