Object Recognition, Detection and 6D Pose Estimation
State of the Art Methods and Datasets
Accurate localization and pose estimation of 3D objects is of great importance to many higher level tasks such as robotic manipulation (like Amazon Picking Challenge), scene interpretation and augmented reality to name a few. The recent introduction of consumer-level depth sensors have allowed for substantial improvement over traditional 2D approaches as finer 3D geometrical features can be captured. However, there still remain several challenges to address including:
- Foreground occlusions
- Background clutter
- Large scale and pose changes
- Multi-instace objects
The scope of this website is to list state of the art methods and datasets available to further help drive research.
State of the Art Methods
| Title | Year | Notes | ||
| Pose Guided RGBD Feature Learning for 3D Object Pose Estimation V. Balntas, A. Doumanoglou, C. Sahin, J. Sock, R. Kouskouridas, T-K Kim | ICCV 2017 | paper | ||
| Real-Time Monocular Pose Estimation of 3D Objects Using Temporally Consistent Local Color Histograms H. Tjaden, U. Schwanecke, E. Schomer | ICCV 2017 | paper – video | ||
| Rotational Subgroup Voting and Pose Clustering for Robust 3D Object Recognition A. G. Buch, L. Kiforenko, D. Kraft | ICCV 2017 | paper | ||
| SSD-6D- Making RGB-Based 3D Detection and 6D Pose Estimation Great Again W. Kehl, F. Manhardt, F. Tombari, S. Ilic, N. Navab | ICCV 2017 | paper – supplementary material | ||
| BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using Depth M. Rad, V. Lepetit | ICCV 2017 | paper – project | ||
| BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using Depth M. Rad, V. Lepetit | ICCV 2017 | paper – project | ||
| Global Hypothesis Generation for 6D Object Pose Estimation F. Michel, A. Kirillov, E. Brachmann, A. Krull, S. Gumhold, B. Savchynskyy, C. Rother | CVPR 2017 | paper | ||
| PoseAgent-Budget-Constrained 6D Object Pose Estimation via Reinforcement Learning A. Krull, E. Brachmann, S. Nowozin, F. Michel, J. Shotton, C. Rother | CVPR 2017 | paper | ||
| Going Further with Point Pair Features S. Hinterstoisser, V. Lepetit, N. Rajkumar, K. Konolige | ECCV 2016 | paper | ||
| Deep Learning of Local RGB-D Patches for 3D Object Detection and 6D Pose Estimation W. Kehl, F. Milletari, F. Tombari, S. Ilic, N. Navab | ECCV 2016 | paper – video | ||
| Uncertainty-Driven 6D Pose Estimation of Objects and Scenes from a Single RGB Image E. Brachmann, F. Michel, A. Krull, M. Y. Yang, S. Gumhold, C. Rother | CVPR 2016 | paper – project page | ||
| Recovering 6D Object Pose and Predicting Next-Best-View in the Crowd A. Doumanoglou, R. Kouskouridas, S. Malassiotis, T-K, Kim | CVPR 2016 | paper – project page – supplementary material | ||
| Learning Analysis-by-Synthesis for 6D Pose Estimation in RGB-D Images A. Krull, E. Brachmann, F. Michel, M. Y. Yang, S. Gumhold, C. Rother | ICCV 2015 | paper – project page – video | ||
| A Novel Representation of Parts for Accurate 3D Object Detection and Tracking in Monocular Images A. Crivellaro, M. Rad, Y. Verdie, K. M. Yi and P. Fua, V. Lepetit | ICCV 2015 | paper – supplementary material | ||
| Detection and Fine 3D Pose Estimation of Texture-less Objects in RGB-D Images T. Hodaň, X. Zabulis, M. Lourakis, Š. Obdržálek, J. Matas | IROS 2015 | paper | ||
| Learning Descriptors for Object Recognition and 3D Pose Estimation P. Wohlhart, V. Lepetit | CVPR 2015 | paper – project page – video | ||
| Hashmod : A Hashing Method for Scalable 3D Object Detection W. Kehl, F. Tombari, N. Navab, S. Ilic, V. Lepetit | BMVC 2015 | paper | ||
| Pose Estimation of Kinematic Chain Instances via Object Coordinate Regression F. Michel, A. Krull, E. Brachmann, M. Y. Yang, S. Gumhold, C. Rother | BMVC 2015 | paper – project page – supplementary material – extended abstract | ||
| Latent-Class Hough Forests for 3D Object Detection and Pose Estimation A. Tejani, D. Tang, R. Kouskouridas, T-K. Kim | ECCV 2014 | paper – project page – spotlight – video 1 – video 2 | ||
| Learning 6D Object Pose Estimation using 3D Object Coordinates E. Brachmann, A. Krull, F. Michel, S. Gumhold, J. Shotton, C. Rother | ECCV 2014 | paper – project page – supplementary material – spotlight – video 1 | ||
| Robust Instance Recognition in Presence of Occlusion and Clutter U. Bonde, V. Badrinarayanan, R. Cipolla | ECCV 2014 | paper – video | ||
| Model Based Training, Detection and Pose Estimation of Texture-Less 3D Objects in Heavily Cluttered Scenes S. Hinterstoisser, V. Lepetit, S. Ilic, S. Holzer, G. R. Bradski, K. Konolige, N. Navab | ACCV 2012 | paper – video | ||
| Model Globally, Match Locally: Efficient and Robust 3D Object Recognition B. Drost, M. Ulrich, N. Navab, S. Ilic | CVPR 2010 | paper | ||
Datasets
| Title | Notes | |
| Imperial College London | Multi-instance & Bin-picking Object Dataset | |
| Technical University Dresden | Articulated Object Dataset – Occluded Object Dataset | |
| EPFL & Technical University Graz | 3D Rigid Tracking from RGB Images Dataset | |
| University of Birmingham | Highly Occluded Object Dataset | |
| University of Cambridge | Desk3D Dataset | |
| Rutgers University | Rutgers APC RGB-D Dataset | |
| Technical University of Munich | LINEMOD Dataset | |
| Czech Technical University | T-LESS Dataset | |
Contact
If you believe that something is missing, please contact:
- Rigas Kouskouridas [E-Mail] [Homepage]