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.X.[Qi-Xin],
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
Sun, H.[Haowen],
Wang, T.[Taiyong],
Yu, E.[Enlin],
A dynamic keypoint selection network for 6DoF pose estimation,
IVC(118), 2022, pp. 104372.
Elsevier DOI
2202
6 DoF pose estimation, Dynamic keypoint selection, Scene feature fusion
BibRef
Peng, S.[Sida],
Zhou, X.W.[Xiao-Wei],
Liu, Y.[Yuan],
Lin, H.T.[Hao-Tong],
Huang, Q.X.[Qi-Xing],
Bao, H.J.[Hu-Jun],
PVNet: Pixel-Wise Voting Network for 6DoF Object Pose Estimation,
PAMI(44), No. 6, June 2022, pp. 3212-3223.
IEEE DOI
2205
BibRef
Earlier: A1, A3, A5, A2, A6, Only:
PVNet: Pixel-Wise Voting Network for 6DoF Pose Estimation,
CVPR19(4556-4565).
IEEE DOI
2002
Pose estimation,
Solid modeling, Prediction algorithms,
keypoint detection
BibRef
André, A.N.,
Sandoz, P.,
Jacquot, M.,
Laurent, G.J.,
Pose Measurement at Small Scale by Spectral Analysis of Periodic
Patterns,
IJCV(130), No. 6, June 2022, pp. 1566-1582.
Springer DOI
2207
BibRef
Huang, W.L.[Wei-Lun],
Hung, C.Y.[Chun-Yi],
Lin, I.C.[I-Chen],
Confidence-Based 6D Object Pose Estimation,
MultMed(24), 2022, pp. 3025-3035.
IEEE DOI
2206
Pose estimation, Feature extraction, Image segmentation, Detectors,
Training, Real-time systems, 6-D pose estimation,
deep neural network
BibRef
da Cunha, K.B.[Kelvin B.],
Brito, C.[Caio],
Valença, L.[Lucas],
Figueiredo, L.[Lucas],
Simőes, F.[Francisco],
Teichrieb, V.[Veronica],
The impact of domain randomization on cross-device monocular deep
6DoF detection,
PRL(159), 2022, pp. 224-231.
Elsevier DOI
2206
6DoF pose estimation, Domain randomization, Deep learning, Cross-device
BibRef
Deng, H.[Haowen],
Bui, M.[Mai],
Navab, N.[Nassir],
Guibas, L.J.[Leonidas J.],
Ilic, S.[Slobodan],
Birdal, T.[Tolga],
Deep Bingham Networks: Dealing with Uncertainty and Ambiguity in Pose
Estimation,
IJCV(130), No. 7, July 2022, pp. 1627-1654.
Springer DOI
2207
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
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
Mei, J.[Jianhan],
Jiang, X.D.[Xu-Dong],
Ding, H.H.[Heng-Hui],
Spatial feature mapping for 6DoF object pose estimation,
PR(131), 2022, pp. 108835.
Elsevier DOI
2208
6D Pose estimation, Rotation symmetry, Spherical convolution,
Graph convolutional network
BibRef
Zhou, G.L.[Guang-Liang],
Wang, D.[Deming],
Yan, Y.[Yi],
Chen, H.[Huiyi],
Chen, Q.J.[Qi-Jun],
Semi-Supervised 6D Object Pose Estimation Without Using Real
Annotations,
CirSysVideo(32), No. 8, August 2022, pp. 5163-5174.
IEEE DOI
2208
Pose estimation, Point cloud compression, Annotations,
Feature extraction, Solid modeling, Training, point cloud
BibRef
Dede, M.A.[Muhammet Ali],
Genc, Y.[Yakup],
Object aspect classification and 6DoF pose estimation,
IVC(124), 2022, pp. 104495.
Elsevier DOI
2208
Computer vision, Object pose estimation, Aspect graph, Deep learning
BibRef
Wang, J.C.[Ji-Chun],
Qiu, L.M.[Le-Miao],
Yi, G.D.[Guo-Dong],
Zhang, S.Y.[Shu-You],
Wang, Y.[Yang],
Multiple geometry representations for 6D object pose estimation in
occluded or truncated scenes,
PR(132), 2022, pp. 108903.
Elsevier DOI
2209
Neural network, Pose estimation, Keypoints, Edge vectors,
Symmetry correspondences
BibRef
Shugurov, I.[Ivan],
Zakharov, S.[Sergey],
Ilic, S.[Slobodan],
DPODv2: Dense Correspondence-Based 6 DoF Pose Estimation,
PAMI(44), No. 11, November 2022, pp. 7417-7435.
IEEE DOI
2210
Pose estimation, Detectors, Deep learning, Training data,
Solid modeling, 6 DoF pose estimation, dense correspondences, synthetic data
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
Liu, J.R.[Jie-Rui],
Cao, Z.Q.[Zhi-Qiang],
Tang, Y.[Yingbo],
Liu, X.[Xilong],
Tan, M.[Min],
Category-Level 6D Object Pose Estimation With Structure Encoder and
Reasoning Attention,
CirSysVideo(32), No. 10, October 2022, pp. 6728-6740.
IEEE DOI
2210
Shape, Cognition, Pose estimation, Feature extraction, Decoding,
Solid modeling, Category-level, 6D object pose estimation,
reasoning attention
BibRef
Gao, F.[Fang],
Sun, Q.Y.[Qing-Yi],
Li, S.D.[Shao-Dong],
Li, W.[Wenbo],
Li, Y.[Yong],
Yu, J.[Jun],
Shuang, F.[Feng],
Efficient 6D object pose estimation based on attentive multi-scale
contextual information,
IET-CV(16), No. 7, 2022, pp. 596-606.
DOI Link
2210
BibRef
Liu, J.[Jian],
Sun, W.[Wei],
Liu, C.[Chongpei],
Zhang, X.[Xing],
Fan, S.[Shimeng],
Wu, W.[Wei],
HFF6D: Hierarchical Feature Fusion Network for Robust 6D Object Pose
Tracking,
CirSysVideo(32), No. 11, November 2022, pp. 7719-7731.
IEEE DOI
2211
Feature extraction, Training, Pose estimation, Video sequences,
Robustness, Solid modeling, 6D object pose tracking,
challenging scenes
BibRef
Zou, L.[Lu],
Huang, Z.J.[Zhang-Jin],
Gu, N.[Naijie],
Wang, G.P.[Guo-Ping],
6D-ViT: Category-Level 6D Object Pose Estimation via
Transformer-Based Instance Representation Learning,
IP(31), 2022, pp. 6907-6921.
IEEE DOI
2212
Pose estimation, Shape, Transformers, Solid modeling,
Image reconstruction, Point cloud compression,
representation learning
BibRef
Wang, D.[Deming],
Zhou, G.L.[Guang-Liang],
Yan, Y.[Yi],
Chen, H.[Huiyi],
Chen, Q.J.[Qi-Jun],
GeoPose: Dense Reconstruction Guided 6D Object Pose Estimation With
Geometric Consistency,
MultMed(24), 2022, pp. 4394-4408.
IEEE DOI
2212
Pose estimation, Image reconstruction, Task analysis,
Solid modeling, Feature extraction, Pipelines, geometric consistency
BibRef
Aing, L.[Lee],
Lie, W.N.[Wen-Nung],
Lin, G.S.[Guo-Shiang],
Faster and finer pose estimation for multiple instance objects in a
single RGB image,
IVC(130), 2023, pp. 104618.
Elsevier DOI
2301
6DoF object pose, Multiple instance objects,
Bottom-up approaches, RGB image, 3D coordinate map, Instance masks
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
Wu, C.R.[Chen-Rui],
Chen, L.[Long],
Wang, S.L.[Sheng-Long],
Yang, H.[Han],
Jiang, J.J.[Jun-Jie],
Geometric-aware dense matching network for 6D pose estimation of
objects from RGB-D images,
PR(137), 2023, pp. 109293.
Elsevier DOI
2302
6D pose estimation, Metric learning, Triplet loss,
Dense correspondences, Geometric constraint
BibRef
Li, G.[Guowei],
Zhu, D.C.[Dong-Chen],
Zhang, G.H.[Guang-Hui],
Shi, W.J.[Wen-Jun],
Zhang, T.[Tianyu],
Zhang, X.L.[Xiao-Lin],
Li, J.[Jiamao],
SD-Pose: Structural Discrepancy Aware Category-Level 6D Object Pose
Estimation,
WACV23(5674-5683)
IEEE DOI
2302
Geometry, Point cloud compression, Shape, Fuses, Semantics,
Pose estimation, Algorithms: 3D computer vision
BibRef
Castro, P.[Pedro],
Kim, T.K.[Tae-Kyun],
CRT-6D: Fast 6D Object Pose Estimation with Cascaded Refinement
Transformers,
WACV23(5735-5744)
IEEE DOI
2302
Runtime, Source coding, Pose estimation, Refining, Pipelines,
Transformers, 3D computer vision
BibRef
Gao, D.Y.[Dao-Yi],
Li, Y.T.[Yi-Tong],
Ruhkamp, P.[Patrick],
Skobleva, I.[Iuliia],
Wysocki, M.[Magdalena],
Jung, H.J.[Hyun-Jun],
Wang, P.Y.[Peng-Yuan],
Guridi, A.[Arturo],
Busam, B.[Benjamin],
Polarimetric Pose Prediction,
ECCV22(IX:735-752).
Springer DOI
2211
Explores how complementary polarisation information
influences the accuracy of pose predictions.
BibRef
Kim, D.H.[Dong-Hyun],
Wang, K.H.[Kai-Hong],
Saenko, K.[Kate],
Betke, M.[Margrit],
Sclaroff, S.[Stan],
A Unified Framework for Domain Adaptive Pose Estimation,
ECCV22(XXXIII:603-620).
Springer DOI
2211
WWW Link. Not just for human pose, adapt to others.
BibRef
Xu, L.[Lumin],
Jin, S.[Sheng],
Zeng, W.[Wang],
Liu, W.T.[Wen-Tao],
Qian, C.[Chen],
Ouyang, W.L.[Wan-Li],
Luo, P.[Ping],
Wang, X.G.[Xiao-Gang],
Pose for Everything: Towards Category-Agnostic Pose Estimation,
ECCV22(VI:398-416).
Springer DOI
2211
BibRef
Park, J.[Jaewoo],
Cho, N.I.[Nam Ik],
DProST: Dynamic Projective Spatial Transformer Network for 6D Pose
Estimation,
ECCV22(VI:363-379).
Springer DOI
2211
BibRef
Vutukur, S.R.[Shishir Reddy],
Shugurov, I.[Ivan],
Busam, B.[Benjamin],
Hutter, A.[Andreas],
Ilic, S.[Slobodan],
WeLSA: Learning to Predict 6D Pose from Weakly Labeled Data Using Shape
Alignment,
ECCV22(VIII:645-661).
Springer DOI
2211
BibRef
Lin, J.[Jiehong],
Wei, Z.W.[Ze-Wei],
Ding, C.X.[Chang-Xing],
Jia, K.[Kui],
Category-Level 6D Object Pose and Size Estimation Using Self-supervised
Deep Prior Deformation Networks,
ECCV22(IX:19-34).
Springer DOI
2211
BibRef
Li, H.Y.[Hong-Yang],
Lin, J.[Jiehong],
Jia, K.[Kui],
DCL-Net: Deep Correspondence Learning Network for 6D Pose Estimation,
ECCV22(IX:369-385).
Springer DOI
2211
BibRef
Wen, Y.[Yilin],
Li, X.Y.[Xiang-Yu],
Pan, H.[Hao],
Yang, L.[Lei],
Wang, Z.[Zheng],
Komura, T.[Taku],
Wan, W.P.[Wen-Ping],
DISP6D: Disentangled Implicit Shape and Pose Learning for Scalable 6D
Pose Estimation,
ECCV22(IX:404-421).
Springer DOI
2211
BibRef
Ma, W.[Wufei],
Wang, A.[Angtian],
Yuille, A.[Alan],
Kortylewski, A.[Adam],
Robust Category-Level 6D Pose Estimation with Coarse-to-Fine Rendering
of Neural Features,
ECCV22(IX:492-508).
Springer DOI
2211
BibRef
Liu, Y.[Yuan],
Wen, Y.[Yilin],
Peng, S.[Sida],
Lin, C.[Cheng],
Long, X.X.[Xiao-Xiao],
Komura, T.[Taku],
Wang, W.P.[Wen-Ping],
Gen6D: Generalizable Model-Free 6-DoF Object Pose Estimation from RGB
Images,
ECCV22(XXXII:298-315).
Springer DOI
2211
BibRef
Fan, Z.X.[Zhao-Xin],
Song, Z.[Zhenbo],
Xu, J.[Jian],
Wang, Z.C.[Zhi-Cheng],
Wu, K.[Kejian],
Liu, H.Y.[Hong-Yan],
He, J.[Jun],
Object Level Depth Reconstruction for Category Level 6D Object Pose
Estimation from Monocular RGB Image,
ECCV22(II:220-236).
Springer DOI
2211
BibRef
Hu, Y.L.[Yin-Lin],
Fua, P.[Pascal],
Salzmann, M.[Mathieu],
Perspective Flow Aggregation for Data-Limited 6D Object Pose Estimation,
ECCV22(II:89-106).
Springer DOI
2211
BibRef
Wu, Y.Z.[Yang-Zheng],
Zand, M.[Mohsen],
Etemad, A.[Ali],
Greenspan, M.[Michael],
Vote from the Center: 6 DoF Pose Estimation in RGB-D Images by Radial
Keypoint Voting,
ECCV22(X:335-352).
Springer DOI
2211
BibRef
Zhang, R.[Ruida],
Di, Y.[Yan],
Lou, Z.Q.[Zhi-Qiang],
Manhardt, F.[Fabian],
Tombari, F.[Federico],
Ji, X.Y.[Xiang-Yang],
RBP-Pose: Residual Bounding Box Projection for Category-Level Pose
Estimation,
ECCV22(I:655-672).
Springer DOI
2211
6D pose and 3D size
BibRef
Dang, Z.[Zheng],
Wang, L.[Lizhou],
Guo, Y.[Yu],
Salzmann, M.[Mathieu],
Learning-Based Point Cloud Registration for 6D Object Pose Estimation
in the Real World,
ECCV22(I:19-37).
Springer DOI
2211
BibRef
Chen, K.[Kai],
Cao, R.[Rui],
James, S.[Stephen],
Li, Y.C.[Yi-Chuan],
Liu, Y.H.[Yun-Hui],
Abbeel, P.[Pieter],
Dou, Q.[Qi],
Sim-to-Real 6D Object Pose Estimation via Iterative Self-training for
Robotic Bin Picking,
ECCV22(XXIX:533-550).
Springer DOI
2211
BibRef
Lyu, Y.X.T.[Yang-Xin-Tong],
Royen, R.[Remco],
Munteanu, A.[Adrian],
MONO6D: Monocular Vehicle 6D Pose Estimation with 3D Priors,
ICIP22(2187-2191)
IEEE DOI
2211
Annotations, Pose estimation, Monocular 6DoF pose estimation,
multimodal data processing, deep-learning, vision perception
BibRef
Xu, Y.[Yan],
Lin, K.Y.[Kwan-Yee],
Zhang, G.F.[Guo-Feng],
Wang, X.G.[Xiao-Gang],
Li, H.S.[Hong-Sheng],
RNNPose: Recurrent 6-DoF Object Pose Refinement with Robust
Correspondence Field Estimation and Pose Optimization,
CVPR22(14860-14870)
IEEE DOI
2210
Training, Solid modeling, Recurrent neural networks,
Computational modeling, Pose estimation, Robot vision,
Vision applications and systems
BibRef
Merrill, N.[Nathaniel],
Guo, Y.L.[Yu-Liang],
Zuo, X.X.[Xing-Xing],
Huang, X.Y.[Xin-Yu],
Leutenegger, S.[Stefan],
Peng, X.[Xi],
Ren, L.[Liu],
Huang, G.Q.[Guo-Quan],
Symmetry and Uncertainty-Aware Object SLAM for 6DoF Object Pose
Estimation,
CVPR22(14881-14890)
IEEE DOI
2210
Simultaneous localization and mapping, Current measurement,
Pose estimation, Semantics, Manuals, Robot vision,
Vision applications and systems
BibRef
Majcher, M.[Mateusz],
Kwolek, B.[Bogdan],
Shape Enhanced Keypoints Learning with Geometric Prior for 6D Object
Pose Tracking,
DLGC22(2985-2991)
IEEE DOI
2210
Geometry, Shape, Quaternions, Pose estimation, Neural networks
BibRef
Jiang, X.[Xiaoke],
Li, D.H.[Dong-Hai],
Chen, H.[Hao],
Zheng, Y.[Ye],
Zhao, R.[Rui],
Wu, L.W.[Li-Wei],
Uni6D: A Unified CNN Framework without Projection Breakdown for 6D
Pose Estimation,
CVPR22(11164-11174)
IEEE DOI
2210
Training, Solid modeling, Head, Electric breakdown, Pose estimation,
Pipelines, RGBD sensors and analytics, Robot vision
BibRef
He, Y.[Yisheng],
Wang, Y.[Yao],
Fan, H.Q.[Hao-Qiang],
Sun, J.[Jian],
Chen, Q.F.[Qi-Feng],
FS6D: Few-Shot 6D Pose Estimation of Novel Objects,
CVPR22(6804-6814)
IEEE DOI
2210
Training, Solid modeling, Costs, Shape, Pose estimation,
Robot vision systems, Prototypes, Pose estimation and tracking,
Transfer/low-shot/long-tail learning
BibRef
Cai, D.[Dingding],
Heikkiä, J.[Janne],
Rahtu, E.[Esa],
OVE6D: Object Viewpoint Encoding for Depth-based 6D Object Pose
Estimation,
CVPR22(6793-6803)
IEEE DOI
2210
Training, Pose estimation, Training data, Optical imaging, Cameras,
Encoding, Pose estimation and tracking, Recognition: detection,
RGBD sensors and analytics
BibRef
Su, Y.Z.[Yong-Zhi],
Saleh, M.[Mahdi],
Fetzer, T.[Torben],
Rambach, J.[Jason],
Navab, N.[Nassir],
Busam, B.[Benjamin],
Stricker, D.[Didier],
Tombari, F.[Federico],
ZebraPose: Coarse to Fine Surface Encoding for 6DoF Object Pose
Estimation,
CVPR22(6728-6738)
IEEE DOI
2210
Training, Measurement, Codes, Pose estimation, Robot vision systems,
Object segmentation, Pose estimation and tracking, Robot vision
BibRef
Lipson, L.[Lahav],
Teed, Z.[Zachary],
Goyal, A.[Ankit],
Deng, J.[Jia],
Coupled Iterative Refinement for 6D Multi-Object Pose Estimation,
CVPR22(6718-6727)
IEEE DOI
2210
Codes, Pose estimation, Benchmark testing, Iterative methods,
Task analysis, Pose estimation and tracking,
Deep learning architectures and techniques
BibRef
Mo, N.[Ningkai],
Gan, W.[Wanshui],
Yokoya, N.[Naoto],
Chen, S.F.[Shi-Feng],
ES6D: A Computation Efficient and Symmetry-Aware 6D Pose Regression
Framework,
CVPR22(6708-6717)
IEEE DOI
2210
Convolutional codes, Measurement, Pose estimation,
Robot vision systems, Data visualization, Robot vision
BibRef
Lin, H.T.[Hai-Tao],
Liu, Z.[Zichang],
Cheang, C.[Chilam],
Fu, Y.W.[Yan-Wei],
Guo, G.D.[Guo-Dong],
Xue, X.Y.[Xiang-Yang],
SAR-Net: Shape Alignment and Recovery Network for Category-level 6D
Object Pose and Size Estimation,
CVPR22(6697-6707)
IEEE DOI
2210
Point cloud compression, Visualization, Shape, Estimation,
Training data, Predictive models, Pose estimation and tracking, Robot vision
BibRef
Cao, T.[Tuo],
Luo, F.[Fei],
Fu, Y.P.[Yan-Ping],
Zhang, W.X.[Wen-Xiao],
Zheng, S.J.[Sheng-Jie],
Xiao, C.X.[Chun-Xia],
DGECN: A Depth-Guided Edge Convolutional Network for End-to-End 6D
Pose Estimation,
CVPR22(3773-3782)
IEEE DOI
2210
Uncertainty, Network topology, Pose estimation, Pipelines,
Robustness, 3D from single images, Robot vision
BibRef
Simpsi, A.[Andrea],
Roggerini, M.[Marco],
Cannici, M.[Marco],
Matteucci, M.[Matteo],
6 DoF Pose Regression via Differentiable Rendering,
CIAP22(II:645-656).
Springer DOI
2205
BibRef
Chen, K.[Kai],
Dou, Q.[Qi],
SGPA: Structure-Guided Prior Adaptation for Category-Level 6D Object
Pose Estimation,
ICCV21(2753-2762)
IEEE DOI
2203
Point cloud compression, Adaptation models, Solid modeling,
Service robots, Pose estimation, Semantics,
Vision for robotics and autonomous vehicles
BibRef
Iwase, S.[Shun],
Liu, X.Y.[Xing-Yu],
Khirodkar, R.[Rawal],
Yokota, R.[Rio],
Kitani, K.M.[Kris M.],
RePOSE: Fast 6D Object Pose Refinement via Deep Texture Rendering,
ICCV21(3283-3292)
IEEE DOI
2203
Solid modeling, Runtime, Pose estimation, Image representation,
Multilayer perceptrons, Rendering (computer graphics),
Representation learning
BibRef
Lin, J.H.[Jie-Hong],
Wei, Z.W.[Ze-Wei],
Li, Z.H.[Zhi-Hao],
Xu, S.C.[Song-Cen],
Jia, K.[Kui],
Li, Y.Q.[Yuan-Qing],
DualPoseNet: Category-level 6D Object Pose and Size Estimation Using
Dual Pose Network with Refined Learning of Pose Consistency,
ICCV21(3540-3549)
IEEE DOI
2203
Training, Convolutional codes, Solid modeling, Shape, Estimation,
Predictive models, Detection and localization in 2D and 3D,
3D from multiview and other sensors
BibRef
Liu, X.Y.[Xing-Yu],
Iwase, S.[Shun],
Kitani, K.M.[Kris M.],
StereOBJ-1M: Large-scale Stereo Image Dataset for 6D Object Pose
Estimation,
ICCV21(10850-10859)
IEEE DOI
2203
Deep learning, Annotations, Pose estimation, Pipelines,
Optimization methods, Lighting, Benchmark testing,
Vision for robotics and autonomous vehicles
BibRef
Di, Y.[Yan],
Manhardt, F.[Fabian],
Wang, G.[Gu],
Ji, X.Y.[Xiang-Yang],
Navab, N.[Nassir],
Tombari, F.[Federico],
SO-Pose: Exploiting Self-Occlusion for Direct 6D Pose Estimation,
ICCV21(12376-12385)
IEEE DOI
2203
Analytical models, Solid modeling, Computational modeling,
Pose estimation, Robustness,
Vision for robotics and autonomous vehicles
BibRef
Zhou, G.Y.[Guang-Yuan],
Wang, H.Q.[Hui-Qun],
Chen, J.X.[Jia-Xin],
Huang, D.[Di],
PR-GCN: A Deep Graph Convolutional Network with Point Refinement for
6D Pose Estimation,
ICCV21(2773-2782)
IEEE DOI
2203
Point cloud compression, Deep learning, Correlation, Convolution,
Pose estimation, Detection and localization in 2D and 3D,
Vision for robotics and autonomous vehicles
BibRef
Höfer, T.[Timon],
Shamsafar, F.[Faranak],
Benbarka, N.[Nuri],
Zell, A.[Andreas],
Object Detection and Autoencoder-Based 6d Pose Estimation for Highly
Cluttered Bin Picking,
ICIP21(704-708)
IEEE DOI
2201
Service robots, Image processing, Pose estimation,
Object detection, Filtering algorithms, Robot sensing systems,
bin picking
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
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.B.[Shao-Bo],
Zhao, W.Q.[Wan-Qing],
Guan, Z.[Ziyu],
Peng, X.L.[Xian-Lin],
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, J.R.[J. 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.C.[Yu-Chao],
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,
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
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
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
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