12.3.4.3 Pose Estimation -- Range Data

Chapter Contents (Back)
Matching, Range Data. Pose Estimation, Range Data.
See also 6D Object Pose Estimation.

Walas, K.[Krzysztof], Leonardis, A.[Aleš],
UoB highly occluded object challenge (UoB-HOOC),
2016
WWW Link. Dataset, Object Detection. BibRef

Faugeras, O.D., and Hebert, M.,
The Representation, Recognition, and Locating of 3-D Objects,
IJRR(5), No. 3, Fall 1986, pp. 27-52. BibRef 8600
Earlier:
The Representation, Recognition, and Positioning of 3-D Shapes from Range Data,
T3DMP86(13-51). BibRef
Earlier: 3DMV87(301-353). BibRef
And:
A 3-D Recognition and Positing Algorithm Using Geometrical Matching Between Primitive Surfaces,
IJCAI83(996-1002). Recognize Three-Dimensional Objects. Range finder input and a detailed 3-D model (base on range finder data). Match model and image. BibRef

Faugeras, O.D., Hebert, M., Pauchon, E., and Ponce, J.,
Object Representation, Identification, and Positioning from Range Data,
RR-IS84(425-446). BibRef 8400

Faugeras, O.D., Hebert, M., and Pauchon, E.,
Segmentation of Range Data into Planar and Quadratic Patches,
CVPR83(8-13). BibRef 8300

Hebert, M., Kanade, T.,
First Results on Outdoor Scene Analysis Using Range Data,
DARPA85(224-231). BibRef 8500

Hebert, M.,
3-D Landmark Recognition from Range Images,
CVPR92(360-365).
IEEE DOI BibRef 9200

Cole, R., Yap, C.K.,
Shape from Probing,
Algorithms(8), 1987, pp. 19-38. BibRef 8700

Quek, F., Jain, R.C., and Weymouth, T.E.,
An Abstraction-Based Approach to 3-D Pose Determination from Range Images,
PAMI(15), No. 7, July 1993, pp. 722-736.
IEEE DOI Abstraction means feature-based. Compute features of the curves for use in the pose estimation. BibRef 9307

Zhuang, X.H.[Xin-Hua], Huang, Y.[Yan],
Robust 3-D 3-D Pose Estimation,
PAMI(16), No. 8, August 1994, pp. 818-824.
IEEE DOI BibRef 9408
Earlier: ICCV93(567-571).
IEEE DOI BibRef

Burtnyk, N.[Nestor], Greenspan, M.A.[Michael A.],
System for determining the pose of an object which utilizes range profiles and synethic profiles derived from a model,
US_Patent5,471,541, Nov 28, 1995
WWW Link. BibRef 9511

Lavallée, S.[Stéphane], Szeliski, R.S.[Richard S.],
Recovering the Position and Orientation of Free-Form Objects from Image Contours Using 3D Distance Maps,
PAMI(17), No. 4, April 1995, pp. 378-390.
IEEE DOI Octree. Match 3D images (MRI or CT) with 2D X-Ray projections accurately and quickly. Applied to computer assisted surgery. BibRef 9504

Szeliski, R.S.[Richard S.], Lavallée, S.[Stéphane],
Matching 3-D Anatomical Surfaces with Non-Rigid Deformations Using Octree-Splines,
IJCV(18), No. 2, May 1996, pp. 171-186.
Springer DOI 9608
BibRef

Brunie, L.[Lionel], Lavallee, S.[Stephane], Szeliski, R.S.[Richard S.],
Using Force Fields Derived from 3D Distance Maps for Inferring the Attitude of a 3D Rigid Object,
ECCV92(670-675).
Springer DOI BibRef 9200

Lavallee, S.[Stephane], Szeliski, R.S.[Richard S.], Brunie, L.[Lionel],
Matching 3-D Smooth Surfaces with Their 2-D Projections Using 3-D Distance Maps,
SPIE(1570), 1991, pp. 322-336 BibRef 9100

Champleboux, G., Lavallee, S., Szeliski, R.S., and Brunie, L.,
From Accurate Range Imaging Sensor Calibration to Accurate Model-Based 3-D Object Localization,
CVPR92(83-89).
IEEE DOI Match 3-D point data and derive the pose. BibRef 9200

Kemmotsu, K., and Kanade, T.,
Uncertainty in Object Pose Determination with Three Light-Stripe Range Measurements,
RA(11), No. 5, October 1995, pp. 741-747. BibRef 9510
Earlier: CMU-CS-TR-93-100, CMU CS Dept., January 1993. BibRef
And:
Sensor Placement Design for Object Pose Determination with Three Light-Stripe Range Finders,
CMU-CS-TR-94-152, 1994.
PS File. Matching 3-D light-stripe generated images to get the pose. BibRef

Stoddart, A.J., Lemke, S., Hilton, A., Renn, T.,
Estimating Pose Uncertainty for Surface Registration,
IVC(16), No. 2, February 20 1998, pp. 111-120.
Elsevier DOI 9803
BibRef
Earlier: BMVC96(Matching Surfaces). 9608
University of Surrey. All are variants of ICP.
See also Method for Registration of 3-D Shapes, A. BibRef

Sanchiz, J.M.[Jose M.], Fisher, R.B.[Robert Burns],
Viewpoint Estimation in Three-Dimensional Images Taken with Perspective Range Sensors,
PAMI(22), No. 11, November 2000, pp. 1324-1329.
IEEE DOI 0012
BibRef EdinburghFrom 3-D of points of known topology (with noise) BibRef

Greenspan, M.A.[Michael A.],
Geometric Probing of Dense Range Data,
PAMI(24), No. 4, April 2002, pp. 495-508.
IEEE DOI 0204
Pose determination. Hypothesize the pose, search for confirmation. Geometric Probing:
See also Shape from Probing. BibRef

Greenspan, M.A.[Michael A.],
Geometric Probing for 3D Object Recognition in Dense Range Data,
Ph.D.Thesis, Carleton Univ., 1999. BibRef 9900

Lucchese, L.[Luca],
A Frequency Domain Technique Based on Energy Radial Projections for Robust Estimation of Global 2D Affine Transformations,
CVIU(81), No. 1, January 2001, pp. 72-116.
DOI Link 0102
BibRef
And: Corrections: CVIU(82), No. 1, April 2001, pp. 82-83.
DOI Link 0104
BibRef
Earlier:
Estimating Affine Transformations in the Frequency Domain,
ICIP01(II: 909-912).
IEEE DOI 0108
BibRef

Lucchese, L.,
Closed-form pose estimation from metric rectification of coplanar points,
VISP(153), No. 3, June 2006, pp. 364-378.
DOI Link 0608

See also Frequency Domain Technique for Range Data Registration, A. BibRef

Lucchese, L.,
A Hybrid Frequency-space Domain Algorithm for Estimating Projective Transformations of Color Images,
ICIP01(II: 913-916).
IEEE DOI 0108
BibRef

Ünsalan, C.[Cem],
A model based approach for pose estimation and rotation invariant object matching,
PRL(28), No. 1, 1 January 2007, pp. 49-57.
Elsevier DOI 0611
Pose estimation; Shape alignment; Object matching; Implicit polynomials BibRef

Fujimura, K.[Kikuo], Zhu, Y.D.[You-Ding],
Pose estimation based on critical point analysis,
US_Patent7,317,836, Jan 8, 2008
WWW Link. BibRef 0801

Laga, H.[Hamid],
Data-driven approach for automatic orientation of 3D shapes,
VC(27), No. 11, November 2011, pp. 977-989.
WWW Link. 1112
BibRef

Laga, H.[Hamid],
Graspable Parts Recognition in Man-Made 3D Shapes,
ACCV12(II:552-564).
Springer DOI 1304
BibRef

Xia, J.Y.[Jun-Ying], Xu, X.Q.[Xiao-Quan], Zhang, Q.[Qi], Xiong, J.L.[Jiu-Long],
Speeding Up the Orthogonal Iteration Pose Estimation,
IEICE(E95-D), No. 7, July 2012, pp. 1827-1829.
WWW Link. 1208
BibRef

Mooser, R.[René], Forsberg, F.[Fredrik], Hack, E.[Erwin], Székely, G.[Gábor], Sennhauser, U.[Urs],
Estimation of affine transformations directly from tomographic projections in two and three dimensions,
MVA(24), No. 2, February 2013, pp. 419-434.
WWW Link. 1302
Orientations from projections not reconstruction BibRef

Hu, J.X.[Jia-Xi], Hua, J.[Jing],
Pose analysis using spectral geometry,
VC(29), No. 9, September 2013, pp. 949-958.
WWW Link. 1307
3D models represented by meshes. Use spectrum domain defined by Laplace-Beltrami operator. BibRef

Papadakis, P.[Panagiotis],
Enhanced pose normalization and matching of non-rigid objects based on support vector machine modelling,
PR(47), No. 1, 2014, pp. 216-227.
Elsevier DOI 1310
Non-rigid analysis BibRef

Papadakis, P.[Panagiotis], Pirri, F.[Fiora],
Consistent pose normalization of non-rigid shapes using One-Class Support Vector Machines,
NORDIA11(23-30).
IEEE DOI 1106
BibRef

Wang, W.[Wei], Chen, L.L.[Li-Li], Liu, Z.Y.[Zi-Yuan], Kühnlenz, K.[Kolja], Kühnlenz, K.[Kolja], Burschka, D.[Darius],
Textured/textureless object recognition and pose estimation using RGB-D image,
RealTimeIP(10), No. 4, December 2015, pp. 667-682.
Springer DOI 1512
BibRef

Silva do Monte Lima, J.P.[Joăo Paulo], Simőes, F.P.M.[Francisco Paulo Magalhăes], Uchiyama, H.[Hideaki], Teichrieb, V.[Veronica], Marchand, E.[Eric],
Depth-assisted rectification for real-time object detection and pose estimation,
MVA(27), No. 2, February 2016, pp. 193-219.
Springer DOI 1602
BibRef

Oumer, N.W.[Nassir W.], Kriegel, S.[Simon], Ali, H.[Haider], Reinartz, P.[Peter],
Appearance learning for 3D pose detection of a satellite at close-range,
PandRS(125), No. 1, 2017, pp. 1-15.
Elsevier DOI 1703
Satellite pose detection BibRef

Ferrara, P.[Pasquale], Piva, A.[Alessandro], Argenti, F.[Fabrizio], Kusuno, J.Y.[Jun-Ya], Niccolini, M.[Marta], Ragaglia, M.[Matteo], Uccheddu, F.[Francesca],
Wide-angle and long-range real time pose estimation: A comparison between monocular and stereo vision systems,
JVCIR(48), No. 1, 2017, pp. 159-168.
Elsevier DOI 1708
Vision systems BibRef

Jiang, J.[Jian], Wang, G.[Gang], Ho, K.C.,
Accurate Rigid Body Localization via Semidefinite Relaxation Using Range Measurements,
SPLetters(25), No. 3, March 2018, pp. 378-382.
IEEE DOI 1802
Range between anchors and sensors on the body. body area networks, matrix algebra, maximum likelihood estimation, relaxation theory, semidefinite relaxation (SDR) BibRef

Zabulis, X.[Xenophon], Lourakis, M.I.A.[Manolis I. A.], Koutlemanis, P.[Panagiotis],
Correspondence-free pose estimation for 3D objects from noisy depth data,
VC(34), No. 2, February 2018, pp. 193-211.
Springer DOI 1802
Pose from depth data. BibRef

Shen, Y.Q.[Yue-Qian], Lindenbergh, R.[Roderik], Wang, J.G.[Jin-Guo], Ferreira, V.G.[Vagner G.],
Extracting Individual Bricks from a Laser Scan Point Cloud of an Unorganized Pile of Bricks,
RS(10), No. 11, 2018, pp. xx-yy.
DOI Link 1812
BibRef

Li, D.P.[De-Ping], Liu, N.[Ning], Guo, Y.L.[Yu-Lan], Wang, X.M.[Xiao-Ming], Xu, J.[Jin],
3D object recognition and pose estimation for random bin-picking using Partition Viewpoint Feature Histograms,
PRL(128), 2019, pp. 148-154.
Elsevier DOI 1912
Point cloud, Feature descriptor, Bin-picking BibRef

Ke, X.C.[Xiao-Chuan], Wang, Y.[Yue], Huang, L.[Lei],
Three-Dimensional Rigid Body Localization in the Presence of Clock Offsets,
SPLetters(27), 2020, pp. 96-100.
IEEE DOI 2001
Pose using Range. Sensors, Noise measurement, Wireless sensor networks, semi-definite relaxation BibRef

Papaioannidis, C.[Christos], Pitas, I.[Ioannis],
3D Object Pose Estimation Using Multi-Objective Quaternion Learning,
CirSysVideo(30), No. 8, August 2020, pp. 2683-2693.
IEEE DOI 2008
RGB-D data. Pose estimation, Quaternions, Object recognition, Artificial neural networks, Search problems, quaternion BibRef

Papaioannidis, C.[Christos], Mygdalis, V., Pitas, I.[Ioannis],
Domain-Translated 3D Object Pose Estimation,
IP(29), 2020, pp. 9279-9291.
IEEE DOI 2010
Pose estimation, Feature extraction, Training, Task analysis, Training data, synthetic data BibRef

Guo, J.W.[Jian-Wei], Xing, X.J.[Xue-Jun], Quan, W.[Weize], Yan, D.M.[Dong-Ming], Gu, Q.Y.[Qing-Yi], Liu, Y.[Yang], Zhang, X.P.[Xiao-Peng],
Efficient Center Voting for Object Detection and 6D Pose Estimation in 3D Point Cloud,
IP(30), 2021, pp. 5072-5084.
IEEE DOI 2106
Pose estimation, Shape, Object detection, Feature extraction, Object recognition, 3D point cloud BibRef

Li, J.[Jie], Zhuang, Y.Q.[Yi-Qi], Peng, Q.[Qi], Zhao, L.[Liang],
Pose Estimation of Non-Cooperative Space Targets Based on Cross-Source Point Cloud Fusion,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Sun, D.Q.[Dian-Qi], Hu, L.[Liang], Duan, H.X.[Hui-Xian], Pei, H.D.[Hao-Dong],
Relative Pose Estimation of Non-Cooperative Space Targets Using a TOF Camera,
RS(14), No. 23, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Yang, C.X.[Chen-Xi], Zhou, Z.B.[Zhi-Bo], Zhuang, H.Y.[Han-Yang], Wang, C.X.[Chun-Xiang], Yang, M.[Ming],
Global Pose Initialization Based on Gridded Gaussian Distribution With Wasserstein Distance,
ITS(24), No. 5, May 2023, pp. 5094-5104.
IEEE DOI 2305
Point cloud compression, Gaussian distribution, Task analysis, Location awareness, Pose estimation, Laser radar, indoor scenarios BibRef

Zou, L.[Lu], Huang, Z.J.[Zhang-Jin], Gu, N.[Naijie], Wang, G.P.[Guo-Ping],
Learning geometric consistency and discrepancy for category-level 6D object pose estimation from point clouds,
PR(145), 2024, pp. 109896.
Elsevier DOI 2311
6D object pose estimation, 3D object detection, Point cloud processing, Shape recovery BibRef

Li, Y.J.[Yu-Jie], Yin, Z.Y.[Zhi-Yun], Zheng, Y.C.[Yu-Chao], Lu, H.M.[Hui-Min], Kamiya, T.[Tohru], Nakatoh, Y.[Yoshihisa], Serikawa, S.[Seiichi],
Pose Estimation of Point Sets Using Residual MLP in Intelligent Transportation Infrastructure,
ITS(24), No. 11, November 2023, pp. 13359-13369.
IEEE DOI 2311
BibRef

Thalhammer, S.[Stefan], Weibel, J.B.[Jean-Baptiste], Vincze, M.[Markus], Garcia-Rodriguez, J.[Jose],
Self-supervised Vision Transformers for 3D pose estimation of novel objects,
IVC(139), 2023, pp. 104816.
Elsevier DOI 2311
Object pose estimation, Template matching, Vision transformer, Self-supervised learning BibRef

Ye, R.[Ruida], Ren, Y.[Yuan], Zhu, X.Y.[Xiang-Yang], Wang, Y.J.[Yu-Jing], Liu, M.Y.[Ming-Yue], Wang, L.[Lifen],
An Efficient Pose Estimation Algorithm for Non-Cooperative Space Objects Based on Dual-Channel Transformer,
RS(15), No. 22, 2023, pp. 5278.
DOI Link 2311
BibRef


Mallis, D.[Dimitrios], Ali, S.A.[Sk Aziz], Dupont, E.[Elona], Cherenkova, K.[Kseniya], Karadeniz, A.S.[Ahmet Serdar], Khan, M.S.[Mohammad Sadil], Kacem, A.[Anis], Gusev, G.[Gleb], Aouada, D.[Djamila],
SHARP Challenge 2023: Solving CAD History and pArameters Recovery from Point clouds and 3D scans. Overview, Datasets, Metrics, and Baselines,
SHARP23(1778-1787)
IEEE DOI 2401
BibRef

Manousis, T.[Theodoros], Passalis, N.[Nikolaos], Tefas, A.[Anastasios],
Enabling High-Resolution Pose Estimation in Real Time Using Active Perception,
ICIP23(2425-2429)
IEEE DOI 2312
BibRef

Sundermeyer, M.[Martin], Hodan, T.[Tomáš], Labbé, Y.[Yann], Wang, G.[Gu], Brachmann, E.[Eric], Drost, B.[Bertram], Rother, C.[Carsten], Matas, J.G.[Jirí G.],
BOP Challenge 2022 on Detection, Segmentation and Pose Estimation of Specific Rigid Objects,
CV4MR23(2785-2794)
IEEE DOI 2309
BibRef

Wang, S.[Sijie], Kang, Q.Y.[Qi-Yu], She, R.[Rui], Wang, W.[Wei], Zhao, K.[Kai], Song, Y.[Yang], Tay, W.P.[Wee Peng],
HypLiLoc: Towards Effective LiDAR Pose Regression with Hyperbolic Fusion,
CVPR23(5176-5185)
IEEE DOI 2309

WWW Link. BibRef

Kadam, P.[Pranav], Zhou, Q.Y.[Qing-Yang], Liu, S.[Shan], Kuo, C.C.J.[C.C. Jay],
PCRP: Unsupervised Point Cloud Object Retrieval and Pose Estimation,
ICIP22(1596-1600)
IEEE DOI 2211
Point cloud compression, Representation learning, Learning systems, Pose estimation, Feature extraction, Registers, successive subspace learning BibRef

Tian, L.[Long], Cavallaro, A.[Andrea], Oh, C.[Changjae],
Cluster-Based 3D Keypoint Detection for Category-Agnostic 6D Pose Tracking,
ICIP22(3651-3655)
IEEE DOI 2211
Point cloud compression, Solid modeling, Target tracking, Image coding, Annotations, Pose estimation, 6D pose tracking, category-agnostic BibRef

Sajnani, R.[Rahul], Poulenard, A.[Adrien], Jain, J.[Jivitesh], Dua, R.[Radhika], Guibas, L.J.[Leonidas J.], Sridhar, S.[Srinath],
ConDor: Self-Supervised Canonicalization of 3D Pose for Partial Shapes,
CVPR22(16948-16958)
IEEE DOI 2210
Point cloud compression, Measurement, Training, Tensors, Image analysis, Shape, Scene analysis and understanding, Self- semi- meta- unsupervised learning BibRef

Lee, T.[Taeyeop], Tremblay, J.[Jonathan], Blukis, V.[Valts], Wen, B.[Bowen], Lee, B.U.[Byeong-Uk], Shin, I.[Inkyu], Birchfield, S.[Stan], Kweon, I.S.[In So], Yoon, K.J.[Kuk-Jin],
TTA-COPE: Test-Time Adaptation for Category-Level Object Pose Estimation,
CVPR23(21285-21295)
IEEE DOI 2309
BibRef

Lee, T.[Taeyeop], Lee, B.U.[Byeong-Uk], Shin, I.[Inkyu], Choe, J.[Jaesung], Shin, U.[Ukcheol], Kweon, I.S.[In So], Yoon, K.J.[Kuk-Jin],
UDA-COPE: Unsupervised Domain Adaptation for Category-level Object Pose Estimation,
CVPR22(14871-14880)
IEEE DOI 2210
Training, Point cloud compression, Solid modeling, Filtering, Pose estimation, Pipelines, Self-supervised learning, Robot vision, Vision applications and systems BibRef

Di, Y.[Yan], Zhang, R.[Ruida], Lou, Z.Q.[Zhi-Qiang], Manhardt, F.[Fabian], Ji, X.Y.[Xiang-Yang], Navab, N.[Nassir], Tombari, F.[Federico],
GPV-Pose: Category-level Object Pose Estimation via Geometry-guided Point-wise Voting,
CVPR22(6771-6781)
IEEE DOI 2210
Point cloud compression, Measurement, Shape, Pose estimation, Real-time systems, Pose estimation and tracking, Scene analysis and understanding BibRef

Sun, J.M.[Jia-Ming], Wang, Z.[Zihao], Zhang, S.[Siyu], He, X.Y.[Xing-Yi], Zhao, H.C.[Hong-Cheng], Zhang, G.F.[Guo-Feng], Zhou, X.W.[Xiao-Wei],
OnePose: One-Shot Object Pose Estimation without CAD Models,
CVPR22(6815-6824)
IEEE DOI 2210
Training, Location awareness, Solid modeling, Visualization, Runtime, Pose estimation, Pose estimation and tracking, Vision applications and systems BibRef

Shugurov, I.[Ivan], Li, F.[Fu], Busam, B.[Benjamin], Ilic, S.[Slobodan],
OSOP: A Multi-Stage One Shot Object Pose Estimation Framework,
CVPR22(6825-6834)
IEEE DOI 2210
Training, Solid modeling, Computational modeling, Pose estimation, Training data, Object detection, Pose estimation and tracking, Transfer/low-shot/long-tail learning BibRef

Musallam, M.A.[Mohamed Adel], Gaudilličre, V.[Vincent], del Castillo, M.O.[Miguel Ortiz], Al Ismaeil, K.[Kassem], Aouada, D.[Djamila],
Leveraging Equivariant Features for Absolute Pose Regression,
CVPR22(6866-6876)
IEEE DOI 2210
Computational modeling, Pose estimation, Training data, Feature extraction, Cameras, Convolutional neural networks, Robot vision BibRef

Nguyen, V.N.[Van Nguyen], Hu, Y.L.[Yin-Lin], Xiao, Y.[Yang], Salzmann, M.[Mathieu], Lepetit, V.[Vincent],
Templates for 3D Object Pose Estimation Revisited: Generalization to New Objects and Robustness to Occlusions,
CVPR22(6761-6770)
IEEE DOI 2210
Training, Solid modeling, Image recognition, Impedance matching, Pose estimation, Pose estimation and tracking, Robot vision BibRef

Zhao, C.[Chen], Ge, Y.X.[Yi-Xiao], Zhu, F.[Feng], Zhao, R.[Rui], Li, H.S.[Hong-Sheng], Salzmann, M.[Mathieu],
Progressive Correspondence Pruning by Consensus Learning,
ICCV21(6444-6453)
IEEE DOI 2203
Location awareness, Learning systems, Computer network reliability, Stacking, Pose estimation, Fitting, BibRef

Yang, H.[Heng], Doran, C.[Chris], Slotine, J.J.[Jean-Jacques],
Dynamical Pose Estimation,
ICCV21(5906-5915)
IEEE DOI 2203
Point cloud compression, Damping, Heuristic algorithms, Pose estimation, Graphics processing units, Stereo, Vision for robotics and autonomous vehicles BibRef

Li, K.[Ke], Wang, S.J.[Shi-Jie], Zhang, X.[Xiang], Xu, Y.F.[Yi-Fan], Xu, W.J.[Wei-Jian], Tu, Z.W.[Zhuo-Wen],
Pose Recognition with Cascade Transformers,
CVPR21(1944-1953)
IEEE DOI 2111
Heating systems, Visualization, Transformers, Pattern recognition, Decoding, Task analysis BibRef

Fischer, K.[Kai], Simon, M.[Martin], Milz, S.[Stefan], Mäder, P.[Patrick],
StickyLocalization: Robust End-To-End Relocalization on Point Clouds using Graph Neural Networks,
WACV22(307-316)
IEEE DOI 2202
Point cloud compression, Training, Runtime, Refining, Pose estimation, Deep Learning BibRef

Fischer, K.[Kai], Simon, M.[Martin], Ölsner, F.[Florian], Milz, S.[Stefan], Groß, H.M.[Horst-Michael], Mäder, P.[Patrick],
StickyPillars: Robust and Efficient Feature Matching on Point Clouds using Graph Neural Networks,
CVPR21(313-323)
IEEE DOI 2111
Deep learning, Runtime, Laser radar, Pose estimation, Pipelines, Transformers BibRef

Müller, N.[Nikolas], Stenzel, J.[Jonas], Chen, J.J.[Jian-Jia],
Self-supervised Detection and Pose Estimation of Logistical Objects in 3D Sensor Data,
ICPR21(10251-10258)
IEEE DOI 2105
Location awareness, Solid modeling, Pose estimation, Robot vision systems, Training data, learning-based vision BibRef

Tatemichi, H.[Hiroki], Kawanishi, Y.[Yasutomo], Deguchi, D.[Daisuke], Ide, I.[Ichiro], Amma, A.[Ayako], Murase, H.[Hiroshi],
Median-Shape Representation Learning for Category-Level Object Pose Estimation in Cluttered Environments,
ICPR21(4473-4480)
IEEE DOI 2105
Pose estimation of an unknown object instance in an object category from a depth image. Training, Shape, Pose estimation, Feature extraction, Image reconstruction BibRef

Grabner, A.[Alexander], Wang, Y.M.[Ya-Ming], Zhang, P.Z.[Pei-Zhao], Guo, P.H.[Pei-Hong], Xiao, T.[Tong], Vajda, P.[Peter], Roth, P.M.[Peter M.], Lepetit, V.[Vincent],
Geometric Correspondence Fields: Learned Differentiable Rendering for 3d Pose Refinement in the Wild,
ECCV20(XVI: 102-119).
Springer DOI 2010
BibRef

Tong, X.W.[Xun-Wei], Li, R.F.[Rui-Feng], Ge, L.Z.[Lian-Zheng], Zhao, L.J.[Li-Jun], Wang, K.[Ke],
Pose Refinement of Occluded 3D Objects Based on Visible Surface Extraction,
ICIVC20(176-181)
IEEE DOI 2009
Iterative closest point algorithm, Surface treatment, Pose estimation, Robustness, Cameras, scene occlusion BibRef

Wang, J.S.[Jia-Shun], Wen, C.[Chao], Fu, Y.W.[Yan-Wei], Lin, H.T.[Hai-Tao], Zou, T.Y.[Tian-Yun], Xue, X.Y.[Xiang-Yang], Zhang, Y.D.[Yin-Da],
Neural Pose Transfer by Spatially Adaptive Instance Normalization,
CVPR20(5830-5838)
IEEE DOI 2008
Code, Mesh Pose.
WWW Link. Shape, Feature extraction, Strain, Decoding, Machine learning, Task analysis BibRef

Yang, Z.P.[Zhen-Pei], Yan, S.M.[Si-Ming], Huang, Q.X.[Qi-Xing],
Extreme Relative Pose Network Under Hybrid Representations,
CVPR20(2452-2461)
IEEE DOI 2008
Feature extraction, Pose estimation, Robustness, Layout, Pipelines BibRef

Liu, X.F.[Xiao-Feng], Zou, Y.[Yang], Che, T.[Tong], Jia, P.[Ping], Ding, P.[Peng], You, J.[Jane], Kumar, B.V.K.V.[B.V.K. Vijaya],
Conservative Wasserstein Training for Pose Estimation,
ICCV19(8261-8271)
IEEE DOI 2004
head, body, vehicle and 3D object pose. entropy, learning (artificial intelligence), object detection, optimisation, pose estimation, BibRef

Yang, Z.P.[Zhen-Pei], Pan, J.Z.[Jeffrey Z.], Luo, L.J.[Lin-Jie], Zhou, X.W.[Xiao-Wei], Grauman, K.[Kristen], Huang, Q.X.[Qi-Xing],
Extreme Relative Pose Estimation for RGB-D Scans via Scene Completion,
CVPR19(4526-4535).
IEEE DOI 2002
BibRef

Rasmussen, F.N.[Frederik Nřrby], Andersen, S.T.[Sebastian Terp], Grossmann, B.[Bjarne], Boukas, E.[Evangelos], Nalpantidis, L.[Lazaros],
Planar Pose Estimation Using Object Detection and Reinforcement Learning,
CVS19(353-365).
Springer DOI 1912
BibRef

Alexandrov, S.V.[Sergey V.], Patten, T.[Timothy], Vincze, M.[Markus],
Leveraging Symmetries to Improve Object Detection and Pose Estimation from Range Data,
CVS19(397-407).
Springer DOI 1912
BibRef

Thalhammer, S., Patten, T.[Timothy], Vincze, M.[Markus],
SyDPose: Object Detection and Pose Estimation in Cluttered Real-World Depth Images Trained using Only Synthetic Data,
3DV19(106-115)
IEEE DOI 1911
Pose estimation, Training, Task analysis, Deep learning, Solid modeling, depth data BibRef

Gao, G.[Ge], Lauri, M.[Mikko], Zhang, J.W.[Jian-Wei], Frintrop, S.[Simone],
Occlusion Resistant Object Rotation Regression from Point Cloud Segments,
4DPose18(I:716-729).
Springer DOI 1905
BibRef

Wang, Y.M.[Ya-Ming], Tan, X.[Xiao], Yang, Y.[Yi], Li, Z., Liu, X., Zhou, F., Davis, L.S.,
A Refined 3D Pose Dataset for Fine-Grained Object Categories,
R6D19(2797-2806)
IEEE DOI 2004
Dataset, Object Recognition.
HTML Version. image segmentation, object recognition, pose estimation, statistical analysis, image segmentation networks, IoU, Fine grained objects BibRef

Wang, Y.M.[Ya-Ming], Tan, X.[Xiao], Yang, Y.[Yi], Liu, X.[Xiao], Ding, E.[Errui], Zhou, F.[Feng], Davis, L.S.[Larry S.],
3D Pose Estimation for Fine-Grained Object Categories,
4DPose18(I:619-632).
Springer DOI 1905
BibRef

Mahendran, S.[Siddharth], Ali, H.[Haider], Vidal, R.[René],
Convolutional Networks for Object Category and 3D Pose Estimation from 2D Images,
4DPose18(I:698-715).
Springer DOI 1905
BibRef

g Poier, G.[Georg], Opitz, M.[Michael], Schinagl, D.[David], Bischof, H.[Horst],
MURAUER: Mapping Unlabeled Real Data for Label AUstERity,
WACV19(1393-1402)
IEEE DOI 1904
learning (artificial intelligence), pose estimation, mapping unlabeled real data for label austerity, MURAUER, Neural networks BibRef

He, X.W.[Xin-Wei], Zhou, Y.[Yang], Zhou, Z.C.[Zhi-Chao], Bai, S.[Song], Bai, X.[Xiang],
Triplet-Center Loss for Multi-view 3D Object Retrieval,
CVPR18(1945-1954)
IEEE DOI 1812
Shape, Solid modeling, Feature extraction, Benchmark testing, Task analysis BibRef

Rosa, S.[Stefano], Toscana, G.[Giorgio],
Fast Feature-Less Quaternion-based Particle Swarm Optimization for Object Pose Estimation From RGB-D Images,
BMVC16(xx-yy).
HTML Version. 1805
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Jafari, O.H.[Omid Hosseini], Mustikovela, S.K.[Siva Karthik], Pertsch, K.[Karl], Brachmann, E.[Eric], Rother, C.[Carsten],
iPose: Instance-Aware 6D Pose Estimation of Partly Occluded Objects,
ACCV18(III:477-492).
Springer DOI 1906
BibRef

Lin, M., Lin, L., Liang, X., Wang, K., Cheng, H.,
Recurrent 3D Pose Sequence Machines,
CVPR17(5543-5552)
IEEE DOI 1711
Feature extraction, Geometry, Image sequences, Pose estimation, Solid modeling, BibRef

Mousavian, A., Anguelov, D., Flynn, J., Košecká, J.,
3D Bounding Box Estimation Using Deep Learning and Geometry,
CVPR17(5632-5640)
IEEE DOI 1711
Object detection, Pose estimation, Shape, Solid modeling BibRef

Rink, C.[Christian], Kriegel, S.[Simon],
Streaming Monte Carlo Pose Estimation for Autonomous Object Modeling,
CRV16(156-163)
IEEE DOI 1612
3D modeling; Active sensing; Laser scanning; Pose estimation BibRef

Zamir, A.R.[Amir R.], Wekel, T.[Tilman], Agrawal, P.[Pulkit], Wei, C.[Colin], Malik, J.[Jitendra], Savarese, S.[Silvio],
Generic 3D Representation via Pose Estimation and Matching,
ECCV16(III: 535-553).
Springer DOI 1611
BibRef

Srinivasan, R.R.[Ranga Ramanujam], Xia, Z.Y.[Zheng-Yu], Kim, J.[Joohee], Park, Y.S.[Young Soo],
Confidence indicators based pose estimation for high-quality 3D reconstruction using depth image,
VCIP15(1-4)
IEEE DOI 1605
Anisotropic magnetoresistance BibRef

Papon, J.[Jeremie], Schoeler, M.[Markus],
Semantic Pose Using Deep Networks Trained on Synthetic RGB-D,
ICCV15(774-782)
IEEE DOI 1602
Furniture, indoor. Adaptation models BibRef

Mottaghi, R.[Roozbeh], Xiang, Y.[Yu], Savarese, S.[Silvio],
A coarse-to-fine model for 3D pose estimation and sub-category recognition,
CVPR15(418-426)
IEEE DOI 1510
BibRef

Zach, C.[Christopher], Penate-Sanchez, A.[Adrian], Pham, M.T.[Minh-Tri],
A dynamic programming approach for fast and robust object pose recognition from range images,
CVPR15(196-203)
IEEE DOI 1510
BibRef

Großmann, B.[Bjarne], Siam, M.[Mennatullah], Krüger, V.[Volker],
Comparative Evaluation of 3D Pose Estimation of Industrial Objects in RGB Pointclouds,
CVS15(329-342).
Springer DOI 1507
BibRef

Nguyen, D.D.[Duc Dung], Ko, J.P.[Jae Pil], Jeon, J.W.[Jae Wook],
Determination of 3D object pose in point cloud with CAD model,
FCV15(1-6)
IEEE DOI 1506
feature extraction BibRef

Shimizu, S., Koyasu, H., Kobayashi, Y., Kuno, Y.,
Object pose estimation using category information from a single image,
FCV15(1-4)
IEEE DOI 1506
computer vision BibRef

Andreux, M.[Mathieu], Rodolŕ, E.[Emanuele], Aubry, M.[Mathieu], Cremers, D.[Daniel],
Anisotropic Laplace-Beltrami Operators for Shape Analysis,
NORDIA14(299-312).
Springer DOI 1504
BibRef

Guzman-Rivera, A.[Abner], Kohli, P.[Pushmeet], Glocker, B.[Ben], Shotton, J.[Jamie], Sharp, T.[Toby], Fitzgibbon, A.W.[Andrew W.], Izadi, S.[Shahram],
Multi-output Learning for Camera Relocalization,
CVPR14(1114-1121)
IEEE DOI 1409
Multi-output learning; camera relocalization; diverse predictions The pose of a camera relative to a known 3D scene with RGB-D image. BibRef

Shotton, J.D.J.[Jamie D.J.], Glocker, B.[Ben], Zach, C.[Christopher], Izadi, S.[Shahram], Criminisi, A.[Antonio], Fitzgibbon, A.W.[Andrew W.],
Scene Coordinate Regression Forests for Camera Relocalization in RGB-D Images,
CVPR13(2930-2937)
IEEE DOI 1309
Infer pose relative to known 3D scene.
See also TextonBoost for Image Understanding: Multi-Class Object Recognition and Segmentation by Jointly Modeling Texture, Layout, and Context. BibRef

Barnea, E.[Ehud], Ben-Shahar, O.[Ohad],
Contextual Object Detection with a Few Relevant Neighbors,
ACCV18(II:480-495).
Springer DOI 1906
BibRef
And:
Depth Based Object Detection from Partial Pose Estimation of Symmetric Objects,
ECCV14(V: 377-390).
Springer DOI 1408
Partial pose from depth, match. BibRef

Breslav, M.[Mikhail], Hedrick, T.L., Sclaroff, S.[Stan], Betke, M.[Margrit],
Discovering useful parts for pose estimation in sparsely annotated datasets,
WACV16(1-9)
IEEE DOI 1606
Animals BibRef

Breslav, M.[Mikhail], Fuller, N.[Nathan], Sclaroff, S.[Stan], Betke, M.[Margrit],
3D pose estimation of bats in the wild,
WACV14(91-98)
IEEE DOI 1406
Cameras BibRef

Zhai, D.[Deming], Chang, H.[Hong], Chen, X.L.[Xi-Lin], Gao, W.[Wen],
Instance-specific canonical correlation analysis for pose alignment,
ICIP13(2544-2547)
IEEE DOI 1402
BibRef

Kurmankhojayev, D.[Daniyar], Hasler, N.[Nils], Theobalt, C.[Christian],
Monocular Pose Capture with a Depth Camera Using a Sums-of-Gaussians Body Model,
GCPR13(415-424).
Springer DOI 1311
BibRef

Thachasongtham, D.[Dissaphong], Yoshida, T.[Takumi], de Sorbier, F.[François], Saito, H.[Hideo],
3D Object Pose Estimation Using Viewpoint Generative Learning,
SCIA13(512-521).
Springer DOI 1311
BibRef

El-Gaaly, T.[Tarek], Torki, M.[Marwan],
RGBD object pose recognition using local-global multi-kernel regression,
ICPR12(2468-2471).
WWW Link. 1302
BibRef

Produit, T.[Timothee], Tuia, D.[Devis], Golay, F.[Francois], Strecha, C.[Christoph],
Pose estimation of landscape images using DEM and orthophotos,
CVRS12(209-214).
IEEE DOI 1302
BibRef

Raytchev, B.[Bisser], Terakado, K.[Kazuya], Tamaki, T.[Toru], Kaneda, K.[Kazufumi],
Pose estimation by local procrustes regression,
ICIP11(3585-3588).
IEEE DOI 1201
BibRef

Axenopoulos, A.[Apostolos], Litos, G.[Georgios], Daras, P.[Petros],
3D model retrieval using accurate pose estimation and view-based similarity,
ICMR11(41).
DOI Link 1301
3D model alignment method, combining two criteria, the plane reflection symmetry and rectilinearity. BibRef

Zhang, Q.[Qian], Jia, J.Y.[Jin-Yuan],
A GPU Based High-Efficient And Accurate Optimal Pose Alignment Approach Of 3d Objects,
3DOR11(97-100)
DOI Link 1301
BibRef

Fenzi, M.[Michele], Leal-Taixe, L.[Laura], Ostermann, J.[Jorn], Tuytelaars, T.,
Continuous Pose Estimation with a Spatial Ensemble of Fisher Regressors,
ICCV15(1035-1043)
IEEE DOI 1602
Design automation BibRef

Fenzi, M.[Michele], Leal-Taixe, L.[Laura], Schindler, K.[Konrad], Ostermann, J.[Jorn],
Pose Estimation of Object Categories in Videos Using Linear Programming,
WACV15(821-828)
IEEE DOI 1503
Estimation BibRef

Fenzi, M.[Michele], Ostermann, J.[Jorn],
Embedding Geometry in Generative Models for Pose Estimation of Object Categories,
BMVC14(xx-yy).
HTML Version. 1410
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Fenzi, M.[Michele], Leal-Taixe, L.[Laura], Rosenhahn, B.[Bodo], Ostermann, J.[Jorn],
Class Generative Models Based on Feature Regression for Pose Estimation of Object Categories,
CVPR13(755-762)
IEEE DOI 1309
Continuous pose estimation BibRef

Soltow, E.[Erik], Rosenhahn, B.[Bodo],
Automatic Pose Estimation Using Contour Information from X-Ray Images,
MCBMIIA15(246-257).
Springer DOI 1603
BibRef

Fenzi, M.[Michele], Dragon, R.[Ralf], Leal-Taixé, L.[Laura], Rosenhahn, B.[Bodo], Ostermann, J.[Jörn],
3d Object Recognition and Pose Estimation for Multiple Objects Using Multi-Prioritized Ransac and Model Updating,
DAGM12(123-133).
Springer DOI 1209
BibRef

Persad, R.A., Armenakis, C., Sohn, G.,
Integration of Video Images and CAD Wireframes for 3d Object Localization,
AnnalsPRS(I-3), No. 2012, pp. 353-358.
DOI Link 1209
BibRef

Ali, H.[Haider], Figueroa, N.[Nadia],
Segmentation and Pose Estimation of Planar Metallic Objects,
CRV12(376-382).
IEEE DOI 1207
Segmentation by euclidean clustering, pose estimation by ICP. Planar surfaces in laser scanner data. BibRef

Aldoma, A., Vincze, M.,
Pose Alignment for 3D Models and Single View Stereo Point Clouds Based on Stable Planes,
3DIMPVT11(374-380).
IEEE DOI 1109
BibRef

Bey, A.[Aurélien], Chaine, R.[Raphaëlle], Marc, R.[Raphaël], Thibault, G.[Guillaume], Akkouche, S.[Samir],
Reconstruction of Consistent 3D CAD Models from Point Cloud Data Using A Priori CAD Models,
Laser11(xx-yy).
DOI Link 1109
Fitting the point cloud with the 3D model. BibRef

Jia, H.J.[Hong-Jun], Wu, G.R.[Guo-Rong], Wang, Q.[Qian], Shen, D.G.[Ding-Gang],
ABSORB: Atlas building by Self-Organized Registration and Bundling,
CVPR10(2785-2790).
IEEE DOI 1006
Register the model by deforming to each subject. BibRef

Hebel, M., Arens, M., Stilla, U.,
Utilization of 3D City Models and Airborne Laser Scanning for Terrain-based Navigation of Helicopters and UAVs,
CMRT09(187-192).
PDF File. 0909
Use 3D models to determine location. BibRef

Selby, B.P., Sakas, G., Walter, S., Groch, W.D., Stilla, U.,
Detection of Pose Changes for Spatial Objects from Projective Images,
PIA07(105).
PDF File. 0711
BibRef

Guđmundsson, S.Á.[Sigurjón Árni], Larsen, R.[Rasmus], Ersbřll, B.K.[Bjarne K.],
Robust Pose Estimation Using the SwissRanger SR-3000 Camera,
SCIA07(968-975).
Springer DOI 0706
classify and pose from low res model and 3D data. BibRef

Rodgers, J.[Jim], Anguelov, D.[Dragomir], Pang, H.C.[Hoi-Cheung], Koller, D.[Daphne],
Object Pose Detection in Range Scan Data,
CVPR06(II: 2445-2452).
IEEE DOI 0606
BibRef

Sepp, W., Hirzinger, G.,
Featureless 6 DoF pose refinement from stereo images,
ICPR02(IV: 17-20).
IEEE DOI 0211
BibRef

Rui, L., Hirzinger, G.[Gerd],
Marker-Free Automatic Matching Of Range Data,
PanoPhot05(xx-yy).
PDF File. 0502
BibRef

Amano, T., Hiura, S., Yamaguchi, A., Inokuchi, S.,
Eigenispace Approach for a Pose Detection with Range Images: Robust Pose Detection Method for Pixel Lacks of Range Images,
ICPR96(I: 622-626).
IEEE DOI 9608
(Osaka Univ., J) BibRef

Beveridge, J.R.[J. Ross], and Schwickerath, A.N.A.[Anthony N.A.],
Object to Multisensor Coregistration with Eight Degrees of Freedom,
ARPA94(I:481-490).
PS File. BibRef 9400

Schwickerath, A.N.A.[Anthony N.A.], Beveridge, J.R.[J. Ross],
Coregistration of Range and Optical Images Using Coplanarity and Orientation Constraints,
CVPR96(899-906).
IEEE DOI
PS File. BibRef 9600

Schwickerath, A.N.A., Beveridge, J.R.,
Coregistering 3D Models, Range, and Optical Imagery Using Least-Median Squares Fitting,
ARPA96(719-722).
PS File. BibRef 9600

Pipitone, F., Adams, W.,
Rapid recognition of freeform objects in noisy range images using tripod operators,
CVPR93(715-716).
IEEE DOI 0403
BibRef

Chapter on Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces continues in
6D Object Pose Estimation .


Last update:Mar 16, 2024 at 20:36:19