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
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