12.3.4.3.1 6D Object Pose Estimation

Chapter Contents (Back)
Pose Estimation. 6-D Pose.

Sahin, C.[Caner], Kouskouridas, R.[Rigas], Kim, T.K.[Tae-Kyun],
A learning-based variable size part extraction architecture for 6D object pose recovery in depth images,
IVC(63), No. 1, 2017, pp. 38-50.
Elsevier DOI 1706
Object registration BibRef

Doumanoglou, A.[Andreas], Kouskouridas, R.[Rigas], Malassiotis, S.[Sotiris], Kim, T.K.[Tae-Kyun],
Recovering 6D Object Pose and Predicting Next-Best-View in the Crowd,
CVPR16(3583-3592)
IEEE DOI 1612
BibRef

Zhang, H.[Haoruo], Cao, Q.[Qixin],
Holistic and local patch framework for 6D object pose estimation in RGB-D images,
CVIU(180), 2019, pp. 59-73.
Elsevier DOI 1903
6D object pose estimation, RGB-D images, Holistic patch, Local patch, Convolutional neural network, Particle swarm optimization BibRef

Zhang, X.[Xin], Jiang, Z.G.[Zhi-Guo], Zhang, H.[Haopeng],
Out-of-region keypoint localization for 6D pose estimation,
IVC(93), 2020, pp. 103854.
Elsevier DOI 2001
6D pose estimation, Keypoint representation, Localization confidence, Real-time processing BibRef

Li, Y.[Yi], Wang, G.[Gu], Ji, X.Y.[Xiang-Yang], Xiang, Y.[Yu], Fox, D.[Dieter],
DeepIM: Deep Iterative Matching for 6D Pose Estimation,
IJCV(128), No. 3, March 2020, pp. 657-67.
Springer DOI 2003
BibRef
Earlier: ECCV18(VI: 695-711).
Springer DOI 1810
BibRef

Sundermeyer, M.[Martin], Marton, Z.C.[Zoltan-Csaba], Durner, M.[Maximilian], Triebel, R.[Rudolph],
Augmented Autoencoders: Implicit 3D Orientation Learning for 6D Object Detection,
IJCV(128), No. 3, March 2020, pp. 714-729.
Springer DOI 2003
Train on synthetic date from the objects. BibRef

Sahin, C.[Caner], Garcia-Hernando, G.[Guillermo], Sock, J.[Juil], Kim, T.K.[Tae-Kyun],
A review on object pose recovery: From 3D bounding box detectors to full 6D pose estimators,
IVC(96), 2020, pp. 103898.
Elsevier DOI 2005
BibRef

Li, Q.N.[Qing-Nan], Hu, R.M.[Rui-Min], Xiao, J.[Jing], Wang, Z.Y.[Zhong-Yuan], Chen, Y.[Yu],
Learning latent geometric consistency for 6D object pose estimation in heavily cluttered scenes,
JVCIR(70), 2020, pp. 102790.
Elsevier DOI 2007
Geometric consistency, Geometric reasoning, Pose estimation, Convolutional neural networks BibRef


Chen, W., Duan, J., Basevi, H., Chang, H.J., Leonardis, A.,
PointPoseNet: Point Pose Network for Robust 6D Object Pose Estimation,
WACV20(2813-2822)
IEEE DOI 2006
Pose estimation, Geometry, Feature extraction, Robustness, Pipelines BibRef

Kaskman, R., Zakharov, S., Shugurov, I., Ilic, S.,
HomebrewedDB: RGB-D Dataset for 6D Pose Estimation of 3D Objects,
R6D19(2767-2776)
IEEE DOI 2004
image colour analysis, learning (artificial intelligence), object detection, pose estimation, solid modelling, deep learning, 6D Pose Estimation BibRef

Zakharov, S., Shugurov, I., Ilic, S.,
DPOD: 6D Pose Object Detector and Refiner,
ICCV19(1941-1950)
IEEE DOI 2004
image colour analysis, learning (artificial intelligence), object detection, pose estimation, solid modelling, input image, Training data BibRef

Manhardt, F., Arroyo, D.M., Rupprecht, C., Busam, B., Birdal, T., Navab, N., Tombari, F.,
Explaining the Ambiguity of Object Detection and 6D Pose From Visual Data,
ICCV19(6840-6849)
IEEE DOI 2004
image motion analysis, image texture, object detection, pose estimation, object detection, visual data, pose estimation, BibRef

Park, K., Patten, T., Vincze, M.,
Pix2Pose: Pixel-Wise Coordinate Regression of Objects for 6D Pose Estimation,
ICCV19(7667-7676)
IEEE DOI 2004
image colour analysis, image texture, iterative methods, learning (artificial intelligence), pose estimation, BibRef

Li, Z., Wang, G., Ji, X.,
CDPN: Coordinates-Based Disentangled Pose Network for Real-Time RGB-Based 6-DoF Object Pose Estimation,
ICCV19(7677-7686)
IEEE DOI 2004
computer vision, image colour analysis, neural nets, pose estimation, CDPN, real-time RGB-based 6-DoF object, BibRef

Pitteri, G., Ilic, S., Lepetit, V.,
CorNet: Generic 3D Corners for 6D Pose Estimation of New Objects without Retraining,
R6D19(2807-2815)
IEEE DOI 2004
CAD, edge detection, image colour analysis, image matching, image registration, learning (artificial intelligence), convolutional neural networks BibRef

Lomaliza, J.P.[Jean-Pierre], Park, H.[Hanhoon],
Initial Pose Estimation of 3d Object with Severe Occlusion Using Deep Learning,
ACIVS20(325-336).
Springer DOI 2003
BibRef

Peng, S.[Sida], Liu, Y.[Yuan], Huang, Q.X.[Qi-Xing], Zhou, X.W.[Xiao-Wei], Bao, H.J.[Hu-Jun],
PVNet: Pixel-Wise Voting Network for 6DoF Pose Estimation,
CVPR19(4556-4565).
IEEE DOI 2002
BibRef

Wang, H.[He], Sridhar, S.[Srinath], Huang, J.W.[Jing-Wei], Valentin, J.[Julien], Song, S.[Shuran], Guibas, L.J.[Leonidas J.],
Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation,
CVPR19(2637-2646).
IEEE DOI 2002
BibRef

Chen, B.[Bowen], Bae, J.[Juhan], Mukherjee, D.[Dibyendu],
Fast 6DOF Pose Estimation with Synthetic Textureless CAD Model for Mobile Applications,
ICIP19(2541-2545)
IEEE DOI 1910
Object detection, Pose estimation, Synthetic training, Domain Adaptation, Mobile Platform BibRef

Cunico, F.[Federico], Carletti, M.[Marco], Cristani, M.[Marco], Masci, F.[Fabio], Conigliaro, D.[Davide],
6D Pose Estimation for Industrial Applications,
NTIAP19(374-384).
Springer DOI 1909
From both RGB and depth information. BibRef

Sahin, C.[Caner], Kim, T.K.[Tae-Kyun],
Category-Level 6D Object Pose Recovery in Depth Images,
4DPose18(I:665-681).
Springer DOI 1905
BibRef

Drost, B.[Bertram], Ulrich, M.[Markus], Bergmann, P., Härtinger, P., Steger, C.T.[Carsten T.],
Introducing MVTec ITODD: A Dataset for 3D Object Recognition in Industry,
6DPose17(2200-2208)
IEEE DOI 1802
Dataset, Object Recognition. Cameras, Engines, Gray-scale, Object detection, Sensor phenomena and characterization. BibRef

Knyaz, V.A., Vygolov, O., Kniaz, V.V., Vizilter, Y., Gorbatsevich, V., Luhmann, T., Conen, N.,
Deep Learning of Convolutional Auto-Encoder for Image Matching and 3D Object Reconstruction in the Infrared Range,
6DPose17(2155-2164)
IEEE DOI 1802
Cameras, Convolutional codes, Image matching, Image reconstruction, Robustness, Training BibRef

Sock, J., Kasaei, S.H., Lopes, L.S., Kim, T.K.,
Multi-view 6D Object Pose Estimation and Camera Motion Planning Using RGBD Images,
6DPose17(2228-2235)
IEEE DOI 1802
Cameras, Entropy, Object detection, Object recognition, Planning, Pose estimation BibRef

Balntas, V., Doumanoglou, A., Sahin, C., Sock, J., Kouskouridas, R., Kim, T.K.,
Pose Guided RGBD Feature Learning for 3D Object Pose Estimation,
ICCV17(3876-3884)
IEEE DOI 1802
correlation methods, feature extraction, learning (artificial intelligence), pose estimation, Training BibRef

Krull, A.[Alexander], Brachmann, E.[Eric], Nowozin, S., Michel, F., Shotton, J.[Jamie], Rother, C.[Carsten],
PoseAgent: Budget-Constrained 6D Object Pose Estimation via Reinforcement Learning,
CVPR17(2566-2574)
IEEE DOI 1711
Heuristic algorithms, Pipelines, Pose estimation, Prediction algorithms, Training BibRef

Brachmann, E.[Eric], Krull, A.[Alexander], Michel, F.[Frank], Gumhold, S.[Stefan], Shotton, J.[Jamie],
Learning 6D Object Pose Estimation Using 3D Object Coordinates,
ECCV14(II: 536-551).
Springer DOI 1408
Carsten Rother BibRef

Basevi, H.[Hector], Leonardis, A.[Aleš],
Towards Categorization and Pose Estimation of Sets of Occluded Objects in Cluttered Scenes from Depth Data and Generic Object Models Using Joint Parsing,
6DPose16(III: 665-681).
Springer DOI 1611
BibRef

Nospes, D.[David], Safronov, K.[Kirill], Gillet, S.[Sarah], Brillowski, K.[Klaus], Zimmermann, U.E.[Uwe E.],
Recognition and 6D Pose Estimation of Large-scale Objects using 3D Semi-Global Descriptors,
MVA19(1-6)
DOI Link 1911
E.g. as in navigation. image segmentation, manipulators, mobile robots, object recognition, pose estimation, robot vision, Malware BibRef

Sundermeyer, M.[Martin], Marton, Z.C.[Zoltan-Csaba], Durner, M.[Maximilian], Brucker, M.[Manuel], Triebel, R.[Rudolph],
Implicit 3D Orientation Learning for 6D Object Detection from RGB Images,
ECCV18(VI: 712-729).
Springer DOI 1810
BibRef

Hodan, T.[Tomáš], Michel, F.[Frank], Brachmann, E.[Eric], Kehl, W.[Wadim], Buch, A.G.[Anders Glent], Kraft, D.[Dirk], Drost, B.[Bertram], Vidal, J.[Joel], Ihrke, S.[Stephan], Zabulis, X.[Xenophon], Sahin, C.[Caner], Manhardt, F.[Fabian], Tombari, F.[Federico], Kim, T.K.[Tae-Kyun], Matas, J.G.[Jirí G.],
BOP: Benchmark for 6D Object Pose Estimation,
ECCV18(X: 19-35).
Springer DOI 1810
Dataset, Object Pose. BibRef

Hu, Y.L.[Yin-Lin], Hugonot, J.[Joachim], Fua, P.[Pascal], Salzmann, M.[Mathieu],
Segmentation-Driven 6D Object Pose Estimation,
CVPR19(3380-3389).
IEEE DOI 2002
BibRef

Wang, C.[Chen], Xu, D.[Danfei], Zhu, Y.[Yuke], Martin-Martin, R.[Roberto], Lu, C.[Cewu], Fei-Fei, L.[Li], Savarese, S.[Silvio],
DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion,
CVPR19(3338-3347).
IEEE DOI 2002
BibRef

Richter-Klug, J.[Jesse], Frese, U.[Udo],
Towards Meaningful Uncertainty Information for CNN Based 6d Pose Estimates,
CVS19(408-422).
Springer DOI 1912
Code:
WWW Link. BibRef

Pitteri, G., Ramamonjisoa, M., Ilic, S.[Slobodan], Lepetit, V.[Vincent],
On Object Symmetries and 6D Pose Estimation from Images,
3DV19(614-622)
IEEE DOI 1911
Pose estimation, Training, Measurement, Machine learning algorithms, Deep Learning BibRef

Tombari, F.[Federico], Salti, S.[Samuele], Puglia, L.[Luca], Raiconi, G.[Giancarlo], di Stefano, L.[Luigi],
A Radial Search Method for Fast Nearest Neighbor Search on Range Images,
6DPose16(III: 563-577).
Springer DOI 1611
BibRef

Förstner, W., Khoshelham, K.,
Efficient and Accurate Registration of Point Clouds with Plane to Plane Correspondences,
6DPose17(2165-2173)
IEEE DOI 1802
Iterative closest point algorithm, Maximum likelihood estimation, Motion estimation, Uncertainty BibRef

Hodan, T.[Tomáš], Matas, J.G.[Jirí G.], Obdržálek, Š.[Štepán],
On Evaluation of 6D Object Pose Estimation,
6DPose16(III: 606-619).
Springer DOI 1611
Code, Pose Estimation.
WWW Link. BibRef

Cao, Q., Zhang, H.,
Combined Holistic and Local Patches for Recovering 6D Object Pose,
6DPose17(2219-2227)
IEEE DOI 1802
Feature extraction, Image segmentation, Pose estimation BibRef

Franaszek, M., Cheok, G.S.,
Propagation of Orientation Uncertainty of 3D Rigid Object to Its Points,
6DPose17(2183-2191)
IEEE DOI 1802
Covariance matrices, Eigenvalues and eigenfunctions, Instruments, Matrix converters, Measurement uncertainty, Tracking, Uncertainty BibRef

Park, K., Prankl, J., Vincze, M.,
Mutual Hypothesis Verification for 6D Pose Estimation of Natural Objects,
6DPose17(2192-2199)
IEEE DOI 1802
Pipelines, Pose estimation, Shape, Solid modeling, Training BibRef

Mahendran, S.[Siddharth], Ali, H.[Haider], Vidal, R.[René],
3D Pose Regression Using Convolutional Neural Networks,
6DPose17(2174-2182)
IEEE DOI 1802
BibRef
And: DeepLearnRV17(494-495)
IEEE DOI 1709
Cameras, Network architecture, Pose estimation, Quaternions, Azimuth, Cameras, Solid modeling. Training. BibRef

Buchholz, D.[Dirk], Kubus, D.[Daniel], Winkelbach, S.[Simon], Wahl, F.M.[Friedrich M.],
3D object localization using single camera images,
ICPR12(821-824).
WWW Link. 1302
6D pose from CAD models BibRef

Sahin, C.[Caner], Kim, T.K.[Tae-Kyun],
Recovering 6D Object Pose: A Review and Multi-Modal Analysis,
ACVR18(VI:15-31).
Springer DOI 1905
BibRef

Aldoma, A.[Aitor], Tombari, F.[Federico], Rusu, R.B.[Radu Bogdan], Vincze, M.[Markus],
OUR-CVFH: Oriented, Unique and Repeatable Clustered Viewpoint Feature Histogram for Object Recognition and 6dof Pose Estimation,
DAGM12(113-122).
Springer DOI 1209
BibRef

Aldoma, A.[Aitor], Vincze, M.[Markus], Blodow, N.[Nico], Gossow, D.[David], Gedikli, S.[Suat], Rusu, R.B.[Radu Bogdan], Bradski, G.[Gary],
CAD-model recognition and 6DOF pose estimation using 3D cues,
3DRR11(585-592).
IEEE DOI 1201
BibRef


Last update:Aug 4, 2020 at 13:31:31