12.3.4.3.1 6D Object Pose Estimation

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

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.H.[Jian-Han], 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.B.[Wen-Bo], 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

Zhu, W.J.[Wen-Jun], Feng, H.[Haida], Yi, Y.[Yang], Zhang, M.[Mengyi],
FCR-TrackNet: Towards high-performance 6D pose tracking with multi-level features fusion and joint classification-regression,
IVC(135), 2023, pp. 104698.
Elsevier DOI 2306
Object pose tracking, Deep learning, Feature fusion, Multi-task learning, Classification smoothing label BibRef

Nadeem, U.[Uzair], Bennamoun, M.[Mohammed], Togneri, R.[Roberto], Sohel, F.[Ferdous], Miri-Rekavandi, A.[Aref], Boussaid, F.[Farid],
Cross domain 2D-3D descriptor matching for unconstrained 6-DOF pose estimation,
PR(142), 2023, pp. 109655.
Elsevier DOI 2307
2D-3D Matching, Cross-Domain feature matching, 6-DOF Pose estimation, Image localization, Visual localization BibRef

Fu, D.[Daoyong], Han, S.C.[Song-Chen], Liang, B.B.[Bin-Bin], Li, W.[Wei],
The 6D Pose Estimation of the Aircraft Using Geometric Property,
CirSysVideo(33), No. 7, July 2023, pp. 3358-3368.
IEEE DOI 2307
Aircraft, Skeleton, Pose estimation, Image reconstruction, Aircraft manufacture, Tail, Aircraft 6D pose estimation, geometric property BibRef

Chen, Z.X.[Ze-Xi], Liao, Y.[Yiyi], Du, H.Z.[Hao-Zhe], Zhang, H.D.[Hao-Dong], Xu, X.C.[Xue-Cheng], Lu, H.J.[Hao-Jian], Xiong, R.[Rong], Wang, Y.[Yue],
DPCN++: Differentiable Phase Correlation Network for Versatile Pose Registration,
PAMI(45), No. 12, December 2023, pp. 14366-14384.
IEEE DOI 2311
BibRef

Song, X.G.[Xiao-Gang], Li, H.J.[Hong-Juan], Liang, L.[Li], Shi, W.W.[Wei-Wei], Xie, G.[Guo], Lu, X.F.[Xiao-Feng], Hei, X.H.[Xin-Hong],
TransBoNet: Learning camera localization with Transformer Bottleneck and Attention,
PR(146), 2024, pp. 109975.
Elsevier DOI 2311
Camera localization, 6DoF pose, Hybrid attention, Pose regression BibRef

Liu, Z.Y.[Zhen-Yu], Wang, Q.[Qide], Liu, D.X.[Da-Xin], Tan, J.R.[Jiang-Rong],
PA-Pose: Partial point cloud fusion based on reliable alignment for 6D pose tracking,
PR(148), 2024, pp. 110151.
Elsevier DOI 2402
3D point cloud, Deep learning, Pose tracking, Feature fusion BibRef

Wang, G.[Gu], Manhardt, F.[Fabian], Liu, X.Y.[Xing-Yu], Ji, X.Y.[Xiang-Yang], Tombari, F.[Federico],
Occlusion-Aware Self-Supervised Monocular 6D Object Pose Estimation,
PAMI(46), No. 3, March 2024, pp. 1788-1803.
IEEE DOI 2402
Pose estimation, Rendering (computer graphics), Training, Solid modeling, Annotations, Robustness, 6D object pose estimation, domain adaptation 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

Yu, S.[Sheng], Zhai, D.H.[Di-Hua], Xia, Y.Q.[Yuan-Qing], Li, D.[Dong], Zhao, S.Q.[Shi-Qi],
CatTrack: Single-Stage Category-Level 6D Object Pose Tracking via Convolution and Vision Transformer,
MultMed(26), 2024, pp. 1665-1680.
IEEE DOI 2402
Pose estimation, Transformers, Feature extraction, Task analysis, Object tracking, Target tracking, Pose estimation, pose tracking, transformer BibRef

Feng, G.[Guangkun], Xu, T.B.[Ting-Bing], Liu, F.[Fulin], Liu, M.K.[Ming-Kun], Wei, Z.Z.[Zhen-Zhong],
NVR-Net: Normal Vector Guided Regression Network for Disentangled 6D Pose Estimation,
CirSysVideo(34), No. 2, February 2024, pp. 1098-1113.
IEEE DOI 2402
Pose estimation, Estimation, Cameras, Head, Solid modeling, Feature extraction, Object pose estimation, monocular vision, 3D normal vector BibRef


Wang, R.Q.[Rui-Qi], Wang, X.G.[Xing-Gang], Li, T.[Te], Yang, R.[Rong], Wan, M.[Minhong], Liu, W.Y.[Wen-Yu],
Query6DoF: Learning Sparse Queries as Implicit Shape Prior for Category-Level 6DoF Pose Estimation,
ICCV23(14009-14018)
IEEE DOI Code:
WWW Link. 2401
BibRef

Liu, J.H.[Jian-Hui], Chen, Y.[Yukang], Ye, X.Q.[Xiao-Qing], Qi, X.J.[Xiao-Juan],
IST-Net: Prior-free Category-level Pose Estimation with Implicit Space Transformation,
ICCV23(13932-13942)
IEEE DOI Code:
WWW Link. 2401
BibRef

Zhou, G.Y.[Guang-Yao], Gothoskar, N.[Nishad], Wang, L.[Lirui], Tenenbaum, J.B.[Joshua B.], Gutfreund, D.[Dan], Lázaro-Gredilla, M.[Miguel], George, D.[Dileep], Mansinghka, V.K.[Vikash K.],
3D Neural Embedding Likelihood: Probabilistic Inverse Graphics for Robust 6D Pose Estimation,
ICCV23(21568-21579)
IEEE DOI 2401
BibRef

Castro, P.[Pedro], Kim, T.K.[Tae-Kyun],
PoseMatcher: One-shot 6D Object Pose Estimation by Deep Feature Matching,
R6D23(2140-2149)
IEEE DOI 2401
BibRef

Liu, F.[Fulin], Hu, Y.L.[Yin-Lin], Salzmann, M.[Mathieu],
Linear-Covariance Loss for End-to-End Learning of 6D Pose Estimation,
ICCV23(14061-14071)
IEEE DOI 2401
BibRef

Hai, Y.[Yang], Song, R.[Rui], Li, J.J.[Jiao-Jiao], Ferstl, D.[David], Hu, Y.L.[Yin-Lin],
Pseudo Flow Consistency for Self-Supervised 6D Object Pose Estimation,
ICCV23(14029-14039)
IEEE DOI 2401
BibRef

Wan, B.[Boyan], Shi, Y.F.[Yi-Fei], Xu, K.[Kai],
SOCS: Semantically-aware Object Coordinate Space for Category-Level 6D Object Pose Estimation under Large Shape Variations,
ICCV23(14019-14028)
IEEE DOI Code:
WWW Link. 2401
BibRef

Zhao, H.[Heng], Wei, S.X.[Shen-Xing], Shi, D.[Dahu], Tan, W.M.[Wen-Ming], Li, Z.[Zheyang], Ren, Y.[Ye], Wei, X.[Xing], Yang, Y.[Yi], Pu, S.L.[Shi-Liang],
Learning Symmetry-Aware Geometry Correspondences for 6D Object Pose Estimation,
ICCV23(13999-14008)
IEEE DOI Code:
WWW Link. 2401
BibRef

Lin, J.[Jiehong], Wei, Z.W.[Ze-Wei], Zhang, Y.[Yabin], Jia, K.[Kui],
VI-Net: Boosting Category-level 6D Object Pose Estimation via Learning Decoupled Rotations on the Spherical Representations,
ICCV23(13955-13965)
IEEE DOI 2401
BibRef

Li, F.[Fu], Vutukur, S.R.[Shishir Reddy], Yu, H.[Hao], Shugurov, I.[Ivan], Busam, B.[Benjamin], Yang, S.[Shaowu], Ilic, S.[Slobodan],
NeRF-Pose: A First-Reconstruct-Then-Regress Approach for Weakly-supervised 6D Object Pose Estimation,
R6D23(2115-2125)
IEEE DOI 2401
BibRef

Corsetti, J.[Jaime], Boscaini, D.[Davide], Poiesi, F.[Fabio],
Revisiting Fully Convolutional Geometric Features for Object 6D Pose Estimation,
R6D23(2095-2104)
IEEE DOI Code:
WWW Link. 2401
BibRef

Zhou, J.[Jun], Chen, K.[Kai], Xu, L.L.[Lin-Lin], Dou, Q.[Qi], Qin, J.[Jing],
Deep Fusion Transformer Network with Weighted Vector-Wise Keypoints Voting for Robust 6D Object Pose Estimation,
ICCV23(13921-13931)
IEEE DOI Code:
WWW Link. 2401
BibRef

Jiang, H.[Haobo], Dang, Z.[Zheng], Gu, S.[Shuo], Xie, J.[Jin], Salzmann, M.[Mathieu], Yang, J.[Jian],
Center-Based Decoupled Point Cloud Registration for 6D Object Pose Estimation,
ICCV23(3404-3414)
IEEE DOI Code:
WWW Link. 2401
BibRef

Soler, D.[Diego], Hirata, R.[Roberto], Espadoto, M.[Mateus],
Modeling and Interpreting 6-D Object Pose Estimation,
ICIP23(2325-2329)
IEEE DOI 2312
BibRef

Liu, L.[Liu], Wu, Q.[Qi], Xue, Z.D.[Zhen-Dong], Qian, S.[Sucheng], Li, R.[Rui],
Reaper: Articulated Object 6d Pose Estimation with Deep Reinforcement Learning,
ICIP23(21-25)
IEEE DOI 2312
BibRef

Zheng, L.F.[Lin-Fang], Wang, C.[Chen], Sun, Y.[Yinghan], Dasgupta, E.[Esha], Chen, H.[Hua], Leonardis, A.[Aleš], Zhang, W.[Wei], Chang, H.J.[Hyung Jin],
HS-Pose: Hybrid Scope Feature Extraction for Category-level Object Pose Estimation,
CVPR23(17163-17173)
IEEE DOI 2309

WWW Link. BibRef

Nejatishahidin, N.[Negar], Hutchcroft, W.[Will], Narayana, M.[Manjunath], Boyadzhiev, I.[Ivaylo], Li, Y.G.[Yu-Guang], Khosravan, N.[Naji], Košecká, J.[Jana], Kang, S.B.[Sing Bing],
Graph-CoVis: GNN-Based Multi-View Panorama Global Pose Estimation,
OmniCV23(6459-6468)
IEEE DOI 2309
BibRef

Shi, M.[Min], Huang, Z.[Zihao], Ma, X.Z.[Xian-Zheng], Hu, X.W.[Xiao-Wei], Cao, Z.G.[Zhi-Guo],
Matching Is Not Enough: A Two-Stage Framework for Category-Agnostic Pose Estimation,
CVPR23(7308-7317)
IEEE DOI 2309
BibRef

Chen, H.Z.[Han-Zhi], Manhardt, F.[Fabian], Navab, N.[Nassir], Busam, B.[Benjamin],
TexPose: Neural Texture Learning for Self-Supervised 6D Object Pose Estimation,
CVPR23(4841-4852)
IEEE DOI 2309
BibRef

Hai, Y.[Yang], Song, R.[Rui], Li, J.J.[Jiao-Jiao], Salzmann, M.[Mathieu], Hu, Y.L.[Yin-Lin],
Rigidity-Aware Detection for 6D Object Pose Estimation,
CVPR23(8927-8936)
IEEE DOI 2309
BibRef

Guo, S.[Shuxuan], Hu, Y.L.[Yin-Lin], Alvarez, J.M.[Jose M.], Salzmann, M.[Mathieu],
Knowledge Distillation for 6D Pose Estimation by Aligning Distributions of Local Predictions,
CVPR23(18633-18642)
IEEE DOI 2309
BibRef

Avery, A.[Alexander], Savakis, A.[Andreas],
DeepRM: Deep Recurrent Matching for 6D Pose Refinement,
RHOBIN23(6206-6214)
IEEE DOI 2309
BibRef

Hai, Y.[Yang], Song, R.[Rui], Li, J.J.[Jiao-Jiao], Hu, Y.L.[Yin-Lin],
Shape-Constraint Recurrent Flow for 6D Object Pose Estimation,
CVPR23(4831-4840)
IEEE DOI 2309
BibRef

Zhang, Z.Q.[Zhong-Qun], Chen, W.[Wei], Zheng, L.F.[Lin-Fang], Leonardis, A.[Aleš], Chang, H.J.[Hyung Jin],
Trans6d: Transformer-based 6d Object Pose Estimation and Refinement,
R6D22(112-128).
Springer DOI 2304
BibRef

de Roovere, P.[Peter], Daems, R.[Rembert], Croenen, J.[Jonathan], Bourgana, T.[Taoufik], de Hoog, J.[Joris], Wyffels, F.[Francis],
Cendernet: Center and Curvature Representations for Render-and-compare 6d Pose Estimation,
R6D22(97-111).
Springer DOI 2304
BibRef

Chu, S.[Sunhao], Duan, Y.X.[Yu-Xiao], Schilling, K.[Klaus], Wu, S.[Shufan],
Monocular 6-DoF Pose Estimation for Non-cooperative Spacecrafts Using Riemannian Regression Network,
AI4Space22(186-198).
Springer DOI 2304
BibRef

Salihu, D.[Driton], Steinbach, E.[Eckehard],
SGPCR: Spherical Gaussian Point Cloud Representation and its Application to Object Registration and Retrieval,
WACV23(572-581)
IEEE DOI 2302
Point cloud compression, Training, Solid modeling, Shape, Convolution, Transformers, Algorithms: 3D computer vision BibRef

Li, G.[Guowei], Zhu, D.C.[Dong-Chen], Zhang, G.H.[Guang-Hui], Shi, W.J.[Wen-Jun], Zhang, T.Y.[Tian-Yu], 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.L.[Alan L.], 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.B.[Zhen-Bo], Xu, J.[Jian], Wang, Z.C.[Zhi-Cheng], Wu, K.J.[Ke-Jian], 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.S.[Yi-Sheng], 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.C.[Zi-Chang], 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.S.[Yi-Sheng], 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

Stevšic, 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

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., Šroubek, 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.[Aleš],
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.[Aleš],
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.[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

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

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:Mar 16, 2024 at 20:36:19