YCB-Video,
A large-scale video dataset for 6D object pose estimation. provides
accurate 6D poses of 21 objects from the YCB dataset observed in 92
videos with 133,827 frames.
WWW Link.
Dataset, Pose Estimation.
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
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
Award, ECCV.
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
Jhan, J.P.[Jyun-Ping],
Rau, J.Y.[Jiann-Yeou],
Chou, C.M.[Chih-Ming],
Underwater 3D Rigid Object Tracking and 6-DOF Estimation:
A Case Study of Giant Steel Pipe Scale Model Underwater Installation,
RS(12), No. 16, 2020, pp. xx-yy.
DOI Link
2008
BibRef
Tang, F.,
Wu, Y.,
Hou, X.,
Ling, H.,
3D Mapping and 6D Pose Computation for Real Time Augmented Reality on
Cylindrical Objects,
CirSysVideo(30), No. 9, September 2020, pp. 2887-2899.
IEEE DOI
2009
Cameras, Image reconstruction,
Augmented reality, Tracking, Solid modeling, Feature extraction,
linear P3P RANSAC
BibRef
Dong, Y.C.[Yan-Chao],
Wang, S.B.[Sen-Bo],
Yue, J.G.[Ji-Guang],
Chen, C.[Ce],
He, S.B.[Shi-Bo],
Wang, H.T.[Hao-Tian],
He, B.[Bin],
A Novel Texture-Less Object Oriented Visual SLAM System,
ITS(22), No. 1, January 2021, pp. 36-49.
IEEE DOI
2012
Simultaneous localization and mapping, Feature extraction,
Cameras, Visualization, Object oriented modeling, Solid modeling,
object pose
BibRef
Jiang, Z.H.[Zhi-Hong],
Wang, X.[Xin],
Huang, X.[Xiao],
Li, H.[Hui],
Triangulate geometric constraint combined with visual-flow fusion
network for accurate 6DoF pose estimation,
IVC(108), 2021, pp. 104127.
Elsevier DOI
2104
6D object pose estimation, Iterative translation refinement,
Triangulate geometric constraint, Visual-flow feature fusion
BibRef
Dong, Y.C.[Yan-Chao],
Ji, L.L.[Ling-Ling],
Wang, S.[Senbo],
Gongf, P.[Pei],
Yue, J.G.[Ji-Guang],
Shen, R.J.[Run-Jie],
Chen, C.[Ce],
Zhang, Y.P.[Ya-Ping],
Accurate 6DOF Pose Tracking for Texture-Less Objects,
CirSysVideo(31), No. 5, 2021, pp. 1834-1848.
IEEE DOI
2105
BibRef
Zhu, Y.[Yazhi],
Wan, L.[Lili],
Xu, W.[Wanru],
Wang, S.[Shenghui],
ASPP-DF-PVNet: Atrous Spatial Pyramid Pooling and Distance-Filtered
PVNet for occlusion resistant 6D object pose estimation,
SP:IC(95), 2021, pp. 116268.
Elsevier DOI
2106
6D object pose estimation, Vector fields,
Voting based keypoint localization, Semantic segmentation, ASPP
BibRef
Zhou, G.L.[Guang-Liang],
Yan, Y.[Yi],
Wang, D.[Deming],
Chen, Q.J.[Qi-Jun],
A Novel Depth and Color Feature Fusion Framework for 6D Object Pose
Estimation,
MultMed(23), 2021, pp. 1630-1639.
IEEE DOI
2106
Pose estimation,
Image color analysis, Feature extraction, Color,
region-level feature
BibRef
He, Y.[Yong],
Li, J.[Ji],
Zhou, X.H.[Xuan-Hong],
Chen, Z.W.[Ze-Wei],
Liu, X.[Xin],
Attention Voting Network with Prior Distance Augmented Loss for 6DoF
Pose Estimation,
IEICE(E104-D), No. 7, July 2021, pp. 1039-1048.
WWW Link.
2107
BibRef
Karam, S.,
Lehtola, V.,
Vosselman, G.,
Simple loop closing for continuous 6DOF LIDAR&IMU graph SLAM with
planar features for indoor environments,
PandRS(181), 2021, pp. 413-426.
Elsevier DOI
2110
SLAM, Loop closure, Planar features, Indoor, Point clouds,
Mobile mapping, Laser scanner, LIDAR, 6DOF
BibRef
Mazumder, J.[Joy],
Zand, M.[Mohsen],
Greenspan, M.[Michael],
Multistream Validnet: Improving 6D Object Pose Estimation by
Automatic Multistream Validation,
ICIP21(3143-3147)
IEEE DOI
2201
Training, Image color analysis, Pose estimation, Refining,
Streaming media, Pose estimation, 3D object detection, point cloud, validation
BibRef
Song, J.R.[Jing-Rui],
Hao, S.[Shuling],
Xu, K.[Kefeng],
Uncooperative Satellite 6D Pose Estimation with Relative Depth
Information,
ISVC21(II:166-177).
Springer DOI
2112
BibRef
Aing, L.[Lee],
Lie, W.N.[Wen-Nung],
Chiang, J.C.[Jui-Chiu],
Lin, G.S.[Guo-Shiang],
InstancePose: Fast 6DoF Pose Estimation for Multiple Objects from a
Single RGB Image,
CVinHRC21(2621-2630)
IEEE DOI
2112
Deep learning, Image segmentation,
Semantics, Pose estimation
BibRef
Majcher, M.[Mateusz],
Kwolek, B.[Bogdan],
Deep Quaternion Pose Proposals for 6D Object Pose Tracking,
DSC21(243-251)
IEEE DOI
2112
Quaternions, Neural networks, Optimized production technology,
Prediction algorithms, Cameras, Probability distribution, Particle filters
BibRef
Zhang, S.[Shaobo],
Zhao, W.Q.[Wan-Qing],
Guan, Z.[Ziyu],
Peng, X.[Xianlin],
Peng, J.Y.[Jin-Ye],
Keypoint-graph-driven learning framework for object pose estimation,
CVPR21(1065-1073)
IEEE DOI
2111
Geometry, Training, Solid modeling, Pose estimation, Manuals
BibRef
Wang, G.[Gu],
Manhardt, F.[Fabian],
Tombari, F.[Federico],
Ji, X.Y.[Xiang-Yang],
GDR-Net: Geometry-Guided Direct Regression Network for Monocular 6D
Object Pose Estimation,
CVPR21(16606-16616)
IEEE DOI
2111
Learning systems, Convolutional codes,
Pose estimation, Pipelines, Real-time systems, Pattern recognition
BibRef
Hu, Y.L.[Yin-Lin],
Speierer, S.[Sébastien],
Jakob, W.[Wenzel],
Fua, P.[Pascal],
Salzmann, M.[Mathieu],
Wide-Depth-Range 6D Object Pose Estimation in Space,
CVPR21(15865-15874)
IEEE DOI
2111
Training, Solid modeling, Satellites,
Pose estimation, Scattering, Benchmark testing
BibRef
Yang, Z.X.[Zong-Xin],
Yu, X.[Xin],
Yang, Y.[Yi],
DSC-PoseNet: Learning 6DoF Object Pose Estimation via Dual-scale
Consistency,
CVPR21(3906-3915)
IEEE DOI
2111
Training, Image segmentation, Annotations, Pose estimation, Benchmark testing
BibRef
He, Y.[Yisheng],
Huang, H.B.[Hai-Bin],
Fan, H.Q.[Hao-Qiang],
Chen, Q.F.[Qi-Feng],
Sun, J.[Jian],
FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose
Estimation,
CVPR21(3002-3012)
IEEE DOI
2111
Geometry, Location awareness, Image segmentation,
Pose estimation, Semantics, Object detection
BibRef
Shi, Y.F.[Yi-Fei],
Huang, J.W.[Jun-Wen],
Xu, X.[Xin],
Zhang, Y.F.[Yi-Fan],
Xu, K.[Kai],
StablePose: Learning 6D Object Poses from Geometrically Stable
Patches,
CVPR21(15217-15226)
IEEE DOI
2111
Deep learning,
Pose estimation, Benchmark testing, Stability analysis, Pattern recognition
BibRef
Trabelsi, A.[Ameni],
Chaabane, M.[Mohamed],
Blanchard, N.[Nathaniel],
Beveridge, R.[Ross],
A Pose Proposal and Refinement Network for Better 6D Object Pose
Estimation,
WACV21(2381-2390)
IEEE DOI
2106
Visualization, Runtime,
Computational modeling, Pose estimation, Pipelines
BibRef
Feng, H.[Hangtao],
Zhang, L.[Lu],
Yang, X.[Xu],
Liu, Z.Y.[Zhi-Yong],
MixedFusion: 6D Object Pose Estimation from Decoupled RGB-Depth
Features,
ICPR21(685-691)
IEEE DOI
2105
Measurement, Image color analysis,
Fuses, Pose estimation, Color, Pattern recognition
BibRef
Cheng, Y.[Yi],
Zhu, H.Y.[Hong-Yuan],
Sun, Y.[Ying],
Acar, C.[Cihan],
Jing, W.[Wei],
Wu, Y.[Yan],
Li, L.Y.[Li-Yuan],
Tan, C.[Cheston],
Lim, J.H.[Joo-Hwee],
6D Pose Estimation with Correlation Fusion,
ICPR21(2988-2994)
IEEE DOI
2105
Correlation, Pose estimation, Lighting, Grasping, Benchmark testing,
Task analysis, object pose estimation, RGB-D, correlation fusion
BibRef
Guo, X.[Xiang],
Li, B.[Bo],
Dai, Y.[Yuchao],
Zhang, T.X.[Tong-Xin],
Deng, H.[Hui],
Novel View Synthesis from only a 6-DoF Camera Pose by Two-stage
Networks,
ICPR21(5028-5035)
IEEE DOI
2105
Location awareness, Solid modeling, Visualization,
Robot vision systems, Cameras, Rendering (computer graphics)
BibRef
Stevic, S.,
Hilliges, O.,
Spatial Attention Improves Iterative 6D Object Pose Estimation,
3DV20(1070-1078)
IEEE DOI
2102
Pose estimation, Task analysis, Computational modeling,
Neural networks, Feature extraction,
6D Pose Estimation
BibRef
Sock, J.,
Garcia-Hernando, G.,
Armagan, A.,
Kim, T.K.,
Introducing Pose Consistency and Warp-Alignment for Self-Supervised
6D Object Pose Estimation in Color Images,
3DV20(291-300)
IEEE DOI
2102
Training, Pose estimation,
Solid modeling, Annotations, Cameras, Visualization, self supervised learning
BibRef
Labbé, Y.[Yann],
Carpentier, J.[Justin],
Aubry, M.[Mathieu],
Sivic, J.[Josef],
Single-view robot pose and joint angle estimation via render &
compare,
CVPR21(1654-1663)
IEEE DOI
2111
Training, Visualization, Codes,
Robot vision systems, Collaboration, Estimation
BibRef
Labbé, Y.[Yann],
Carpentier, J.[Justin],
Aubry, M.[Mathieu],
Sivic, J.[Josef],
CosyPose: Consistent Multi-view Multi-object 6d Pose Estimation,
ECCV20(XVII:574-591).
Springer DOI
2011
BibRef
Hagelskjćr, F.[Frederik],
Buch, A.G.[Anders Glent],
Bridging the Reality Gap for Pose Estimation Networks using
Sensor-Based Domain Randomization,
3DODI21(935-944)
IEEE DOI
2112
BibRef
Earlier:
Pointvotenet: Accurate Object Detection And 6 DOF Pose Estimation In
Point Clouds,
ICIP20(2641-2645)
IEEE DOI
2011
Training, Deep learning, Bridges, Pose estimation.
Solid modeling, Image color analysis, Machine learning, Pose estimation
BibRef
Gabas, A.,
Yoshiyasu, Y.,
Singh, R.P.,
Sagawa, R.,
Yoshida, E.,
APE: A More Practical Approach To 6-Dof Pose Estimation,
ICIP20(3164-3168)
IEEE DOI
2011
Robots,
Training, Solid modeling, Neural networks, Cameras, Pose Recognition,
Robot Grasping
BibRef
Chen, X.[Xu],
Dong, Z.J.[Zi-Jian],
Song, J.[Jie],
Geiger, A.[Andreas],
Hilliges, O.[Otmar],
Category Level Object Pose Estimation via Neural Analysis-by-Synthesis,
ECCV20(XXVI:139-156).
Springer DOI
2011
BibRef
Tian, M.[Meng],
Ang, Jr., M.H.[Marcelo H.],
Lee, G.H.[Gim Hee],
Shape Prior Deformation for Categorical 6d Object Pose and Size
Estimation,
ECCV20(XXI:530-546).
Springer DOI
2011
BibRef
Wang, G.[Gu],
Manhardt, F.[Fabian],
Shao, J.Z.[Jian-Zhun],
Ji, X.Y.[Xiang-Yang],
Navab, N.[Nassir],
Tombari, F.[Federico],
Self6d: Self-supervised Monocular 6d Object Pose Estimation,
ECCV20(I:108-125).
Springer DOI
2011
BibRef
Du, J.[Juan],
Wang, R.[Rui],
Cremers, D.[Daniel],
DH3D: Deep Hierarchical 3d Descriptors for Robust Large-scale 6DOF
Relocalization,
ECCV20(IV:744-762).
Springer DOI
2011
BibRef
Park, K.[Kiru],
Patten, T.[Timothy],
Vincze, M.[Markus],
Neural Object Learning for 6d Pose Estimation Using a Few Cluttered
Images,
ECCV20(IV:656-673).
Springer DOI
2011
BibRef
Rozumnyi, D.,
Kotera, J.,
roubek, F.,
Matas, J.,
Sub-Frame Appearance and 6D Pose Estimation of Fast Moving Objects,
CVPR20(6777-6785)
IEEE DOI
2008
Trajectory, Cameras, Shape,
Estimation, Tracking
BibRef
Zhao, W.,
Zhang, S.,
Guan, Z.,
Zhao, W.,
Peng, J.,
Fan, J.,
Learning Deep Network for Detecting 3D Object Keypoints and 6D Poses,
CVPR20(14122-14130)
IEEE DOI
2008
Solid modeling, Feature extraction,
Task analysis, Manuals, Labeling, Object detection
BibRef
Wada, K.,
Sucar, E.,
James, S.,
Lenton, D.,
Davison, A.J.,
MoreFusion: Multi-object Reasoning for 6D Pose Estimation from
Volumetric Fusion,
CVPR20(14528-14537)
IEEE DOI
2008
Feature extraction, Cameras,
Solid modeling, Robot vision systems, Pose estimation
BibRef
Hu, Y.,
Fua, P.,
Wang, W.,
Salzmann, M.,
Single-Stage 6D Object Pose Estimation,
CVPR20(2927-2936)
IEEE DOI
2008
Pose estimation, Feature extraction, Computer architecture,
Network architecture
BibRef
Song, C.,
Song, J.,
Huang, Q.,
HybridPose: 6D Object Pose Estimation Under Hybrid Representations,
CVPR20(428-437)
IEEE DOI
2008
Image edge detection, Pose estimation,
Robustness,
Neural networks
BibRef
Chen, W.,
Jia, X.,
Chang, H.J.,
Duan, J.,
Leonardis, A.,
G2L-Net: Global to Local Network for Real-Time 6D Pose Estimation
With Embedding Vector Features,
CVPR20(4232-4241)
IEEE DOI
2008
Pose estimation, Feature extraction,
Real-time systems, Machine learning, Task analysis
BibRef
Shao, J.,
Jiang, Y.,
Wang, G.,
Li, Z.,
Ji, X.,
PFRL: Pose-Free Reinforcement Learning for 6D Pose Estimation,
CVPR20(11451-11460)
IEEE DOI
2008
Pose estimation, Solid modeling, Training, Task analysis
BibRef
Chen, D.,
Li, J.,
Wang, Z.,
Xu, K.,
Learning Canonical Shape Space for Category-Level 6D Object Pose and
Size Estimation,
CVPR20(11970-11979)
IEEE DOI
2008
Shape, Solid modeling,
Feature extraction, Pose estimation, Agriculture
BibRef
Hodan, T.,
Baráth, D.,
Matas, J.,
EPOS: Estimating 6D Pose of Objects With Symmetries,
CVPR20(11700-11709)
IEEE DOI
2008
Solid modeling,
Robustness, Pose estimation, Systematics, Shape
BibRef
He, Y.,
Sun, W.,
Huang, H.,
Liu, J.,
Fan, H.,
Sun, J.,
PVN3D: A Deep Point-Wise 3D Keypoints Voting Network for 6DoF Pose
Estimation,
CVPR20(11629-11638)
IEEE DOI
2008
Feature extraction, Semantics, Pose estimation, Task analysis,
Clustering algorithms
BibRef
Chen, W.[Wei],
Jia, X.[Xi],
Chang, H.J.[Hyung Jin],
Duan, J.M.[Jin-Ming],
Shen, L.L.[Lin-Lin],
Leonardis, A.[Ale],
FS-Net: Fast Shape-based Network for Category-Level 6D Object Pose
Estimation with Decoupled Rotation Mechanism,
CVPR21(1581-1590)
IEEE DOI
2111
Measurement, Training, Solid modeling,
Convolution, Pose estimation, Training data
BibRef
Chen, W.[Wei],
Duan, J.M.[Jin-Ming],
Basevi, H.,
Chang, H.J.[Hyung Jin],
Leonardis, A.[Ale],
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
image colour analysis, neural nets,
pose estimation, CDPN, real-time RGB-based 6-DoF object,
BibRef
Pitteri, G.[Giorgia],
Bugeau, A.[Aurélie],
Ilic, S.[Slobodan],
Lepetit, V.[Vincent],
3d Object Detection and Pose Estimation of Unseen Objects in Color
Images with Local Surface Embeddings,
ACCV20(I:38-54).
Springer DOI
2103
BibRef
Earlier: A1, A3, A4, Only:
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.W.[Bo-Wen],
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.[Ale],
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
Hodan, T.[Tomá],
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.[Tomá],
Matas, J.G.[Jirí G.],
Obdrálek, .[tepán],
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