12.1.9.1 Point Based Pose Estimation and Recognition

Chapter Contents (Back)
Matching, Points. Pose Estimation, Points.

Birk, J.R.,
A Computation for Robots to Orient and Position Hand Held Workpieces,
SMC(6), No. 10, October 1976, pp. 665-671. BibRef 7610

Kelley, R.B.[Robert B.], Birk, J.R.[John R.], Chen, N.Y.[Nai-Yung],
Estimating Workpiece Pose Using the Feature Points Method,
US_Patent4,402,053, Aug 30, 1983
WWW Link. BibRef 8308
And: A3, A2, A1: DraftManuscript dated Nov 1, 1978 and a revision. BibRef
And: A2, A1, A3:
Visually Estimating Workpiece Pose in a Robot Hand Using the Feature Points Method,
Draft Camera Calibration. Point Matching. No notes on publishing. Match extracted points with feature points (corners and small holes) in the model. Technique involves rotating the object in the robot hand after locating it. BibRef

Birk, J.R., and Kelley, R.B., Chen, N.Y., Wilson, L.,
Image Feature Extraction Using Diameter Limited Gradient Direction Histograms,
PAMI(1), No. 2, April 1979, pp. 228-235. BibRef 7904
Earlier: PRAI-78(xx). (Wrong page numbers.) Extract objects using histograms of edge directions. Find the pose of the objects. BibRef

Tella, R., Birk, J.R., and Kelley, R.B.,
General Purpose Hands for Bin-Picking Robots,
SMC(12), 1982, pp. 828-837. BibRef 8200

Dessimoz, J.D., Birk, J.R., Kelley, R.B., Martins, H.A.S., and I, C.L.[Chi Lin],
Matched Filters for Bin Picking,
PAMI(6), No. 6, November 1984, pp. 686-697. BibRef 8411

Kelley, R.B., Birk, J.R., Martins, H.A.S., Tella, R.,
A Robot System Which Acquires Cylindrical Workpieces from Bins,
SMC(12), 1982, pp. 204-213. BibRef 8200

Kelley, R.B., Martins, H.A.S., Birk, J.R., Dessimoz, J.D.,
Three Vision Algorithms for Acquiring Workpieces from Bins,
PIEEE(71), 1983, pp. 803-820. BibRef 8300

Birk, J.R., and Kelley, R.B., Badami, V.V.,
Workpiece Orientation Correction with a Robot Arm Using Visual Information,
IJCAI77(758). BibRef 7700

Augusteijn, M.F.[Marijke F.], Dyer, C.R.[Charles R.],
Recognition and Recovery of the Three-Dimensional Orientation of Planar Point Patterns,
CVGIP(36), No. 1, October 1986, pp. 76-99.
WWW Link. BibRef 8610
Earlier:
Model-based Shape from Contour and Point Patterns,
CVPR85(100-105). (Univ. of Colorado at Colorado Springs and Univ. of Wisconsin) Recognition, Using Shape. Given a known pattern or shape, compute the surface orientation using an iterative method and no prior correspondence. BibRef

Horn, B.K.P.,
Closed Form Solutions of Absolute Orientation Using Orthonormal Matrices,
JOSA-A(5), No. 7, 1987, pp. 1127-1135. See also Relative Orientation. BibRef 8700

Horn, B.K.P.,
Closed Form Solutions of Absolute Orientation Using Unit Quaternions,
JOSA-A(4), No. 4, April 1987, pp. 629-642. BibRef 8704

Linnainmaa, S.[Seppo], Harwood, D.A.[David], Davis, L.S.,
Pose Determination of a Three-Dimensional Object Using Triangle Pairs,
PAMI(10), No. 5, September 1988, pp. 634-647.
IEEE DOI BibRef 8809
Earlier:
Triangle-Based Pose Determination of 3-D Objects,
ICPR86(116-118). Hough. Pose estimation of a three dimensional object, by a Hough approach using all 6 parameters of the position. Uses triples of points on the object matched to triples of points on the image. Initial experiments on simple objects. BibRef

Pehkonen, K., Harwood, D., Davis, L.S.,
Parallel Calculation of 3-D Pose of a Known Object in a Single View,
PRL(12), 1991, pp. 353-361. BibRef 9100

Walker, M.W.[Michael W.], Shao, L.J.[Le-Jun], Volz, R.A.[Richard A.],
Estimating 3-D Location Parameters Using Dual Number Quaternions,
CVGIP(54), No. 3, November 1991, pp. 358-367.
WWW Link. BibRef 9111

Haralick, R.M., Joo, H., Lee, C.N., Zhuang, X., Vaidya, V.G., and Kim, M.B.,
Pose Estimation from Corresponding Point Data,
SMC(19), No. 6, November/December 1989, pp. 1426-1446. BibRef 8911
Earlier: A1, A3, A4, A5, A6 Only: CVWS87(258-263). Pose Estimation, Evaluation. Closed form solutions for 2-D to 2-D and 3-D to 3-D pose estimations. For perspective 2-D to 3-D, a convergent iterative solution is given, for 2-D perspective to 2-D perspective, a linear solution is given. This is also an argument for error analysis and error propagation analysis. BibRef

Haralick, R.M., Joo, H.,
2D-3D Pose Estimation,
ICPR88(I: 385-391).
IEEE DOI BibRef 8800

Umeyama, S.,
Least-Squares Estimation of Transformation Parameters Between Two Point Patterns,
PAMI(13), No. 4, April 1991, pp. 376-380.
IEEE DOI Follows from Arun and Horn work. Parameterized transformations following from: See also Eigen Decomposition Approach to Weighted Graph Matching Problems, An. BibRef 9104

Umeyama, S.,
Parameterized Point Pattern Matching and Its Application to Recognition of Object Families,
PAMI(15), No. 2, February 1993, pp. 136-144.
IEEE DOI The point positions may be parameterized to allow for some articulation of the parts. See also Eigen Decomposition Approach to Weighted Graph Matching Problems, An. BibRef 9302

Yang, M.C.K., Lee, J.S.,
Object Identification From Multiple Images Based on Point Matching Under A General Transformation,
PAMI(16), No. 7, July 1994, pp. 751-756.
IEEE DOI SAR Imagery. Points are 3-D locations from SAR data. BibRef 9407

Gee, A.[Andrew], Cipolla, R.[Roberto],
Determining the Gaze of Faces in Images,
IVC(12), No. 10, December 1994, pp. 639-647.
WWW Link. Application, Faces. Bayes Nets.
PS File. BibRef 9412
And:
Estimating Gaze from a Single View of a Face,
ICPR94(A:758-760).
IEEE DOI Track features to estimate the pose of the face. BibRef

Gee, A.[Andrew], Cipolla, R.[Roberto],
Fast Visual Tracking by Temporal Consensus,
IVC(14), No. 2, March 1996, pp. 105-114.
WWW Link. 9607
BibRef
Earlier: Cambridge UniversityTechnical Report CUED/F-INFENG/TR 207.
PS File. BibRef

Arun, K.S., Huang, T.S., and Blostein, S.D.,
Least-Squares Fitting of Two 3-D Point Sets,
PAMI(9), No. 5, September 1987, pp. 698-700. This is not strictly motion, but is deriving R and T when given a pair of matching 3-D points. BibRef 8709

DeMenthon, D.F., and Davis, L.S.,
Exact and Approximate Solutions of the Perspective-Three-Point Problem,
PAMI(14), No. 11, November 1992, pp. 1100-1105.
IEEE DOI Match image and model triangles to get pose. BibRef 9211

Oberkampf, D., DeMenthon, D.F., and Davis, L.S.,
Iterative Pose Estimation Using Coplanar Feature Points,
CVIU(63), No. 3, May 1996, pp. 495-511.
DOI Link 9606
BibRef
Earlier:
Iterative Pose Estimation Using Coplanar Points,
CVPR93(626-627).
IEEE DOI POSIT BibRef

DeMenthon, D.F.[Daniel F.],
Computer vision system for position monitoring in three dimensions using non-coplanar light sources attached to a monitored object,
US_Patent5,227,985, Jul 13, 1993
WWW Link. BibRef 9307

DeMenthon, D.F., and Davis, L.S.,
Model-Based Object Pose in 25 Lines of Code,
IJCV(15), No. 1-2, June 1995, pp. 123-141.
Springer DOI BibRef 9506
Earlier: ECCV92(335-343).
Springer DOI BibRef
And: DARPA92(753-761). Of course it is Mathematica code. BibRef

Krishnan, R.[Radha], Sommer, III, H.J., Spidaliere, P.D.[Peter D.],
Monocular Pose of a Rigid Body Using Point Landmarks,
CVGIP(55), No. 3, May 1992, pp. 307-316.
WWW Link. Analysis of the problem and how it is done. BibRef 9205

Chen, S.W.[Sei-Wang], Jain, A.K.[Anil K.],
Strategies of Multi-View and Multi-Matching for 3D Object Recognition,
CVGIP(57), No. 1, January 1993, pp. 121-130.
WWW Link. BibRef 9301

Chen, S.W.[Sei-Wang], Stockman, G.C.[George C.], and Shrikhande, N.[Neelima],
Computing a Pose Hypothesis from a Small Set of 3-D Object Features,
MSU-ENGR-87-001, Department of Computer Science, Michigan State University, 1987. BibRef 8700

Haralick, R.M., Lee, C.N.[Chung-Nan], Ottenberg, K.[Karsten], Nölle, M.[Michael],
Review and Analysis of Solutions of the Three Point Perspective Pose Estimation Problem,
IJCV(13), No. 3, December 1994, pp. 331-356.
Springer DOI BibRef 9412
Earlier:
Analysis and Solutions of the Three Point Perspective Pose Estimation Problem,
CVPR91(592-598).
IEEE DOI BibRef

Wlczek, P., Maccato, A., de Figueiredo, R.J.P.,
Pose Estimation Of 3-Dimensional Objects From Single Camera Images,
DSP(5), No. 3, July 1995, pp. 176-183. BibRef 9507

Wirtz, B., and Maggioni, C.,
3-D Pose Estimation by an Improved Kohonen-Net,
VF91(593-602). A Neural-net apporach for self-organizing feature maps. BibRef 9100

Alter, T.D.,
3-D Pose From 3 Points Using Weak-Perspective,
PAMI(16), No. 8, August 1994, pp. 802-808.
IEEE DOI BibRef 9408
Earlier:
3D Pose from Three Corresponding Points Under Weak-Perspective Projection,
MIT AI Memo-1378, July 1992.
WWW Link. One feasible solution plus reflection. BibRef

Huang, T.S.[Thomas S.], Bruckstein, A.M.[Alfred M.], Holt, R.J.[Robert J.], Netravali, A.N.[Arun N.],
Uniqueness of 3D Pose under Weak Perspective: A Geometrical Proof,
PAMI(17), No. 12, December 1995, pp. 1220-1221.
IEEE DOI Geometric proof of the one feasible solution plus reflection conclusion of See also 3-D Pose From 3 Points Using Weak-Perspective. and See also Recognizing Solid Objects by Alignment with an Image. BibRef 9512

Bruckstein, A.M.[Alfred M.], Holt, R.J.[Robert J.], Huang, T.S.[Thomas S.], Netravali, A.N.[Arun N.],
Optimum Fiducials Under Weak Perspective Projection,
IJCV(35), No. 3, December 1999, pp. 223-244.
DOI Link BibRef 9912
Earlier: ICCV99(67-72).
IEEE DOI BibRef

Bruckstein, A.M.[Alfred M.], Holt, R.J.[Robert J.], Netravali, A.N.[Arun N.],
Iterative algorithm for optimal fiducials under weak perspective projection,
IJIST(19), No. 1, March 2009, pp. 27-36.
DOI Link 0902
BibRef

Horaud, R.[Radu], Dornaika, F.[Fadi], Lamiroy, B.[Bart], Christy, S.[Stephane],
Object Pose: The Link Between Weak Perspective, Paraperspective, and Full Perspective,
IJCV(22), No. 2, March 1997, pp. 173-189.
DOI Link 9706
BibRef
Earlier: A1, A4, A2 only: TRINRIA, September 1994. BibRef

Horaud, R.[Radu], Christy, S.[Stephane], Dornaika, F.[Fadi], Lamiroy, B.[Bart],
Object Pose: Links Between Paraperspective and Perspective,
ICCV95(426-433).
IEEE DOI BibRef 9500

Dornaika, F.[Fadi], Garcia, C.[Christophe],
Pose Estimation using Point and Line Correspondences,
RealTimeImg(5), No. 3, June 1999, pp. 215-230. BibRef 9906
Earlier:
Object pose by affine iterations,
CIAP97(I: 478-485).
Springer DOI 9709
BibRef

Golda, S.[Steven], Rangarajana, A.[Anand], Lua, C.P.[Chien-Ping], Pappua, S.[Suguna], Mjolsnessa, E.[Eric],
New Algorithms for 2D and 3D Point Matching: Pose Estimation and Correspondence,
PR(31), No. 8, August 1998, pp. 1019-1031.
WWW Link. 9807
SoftAssign BibRef

Liu, Y.H.[Yong-Huai], Rodrigues, M.A.[Marcos A.],
Statistical image analysis for pose estimation without point correspondences,
PRL(22), No. 11, September 2001, pp. 1191-1206.
Elsevier DOI 0108
BibRef
Earlier:
Correspondenceless Motion Estimation from Range Images,
ICCV99(654-659).
IEEE DOI BibRef
And:
Using Rigid Constraints to Analyse Motion Parameters from Two Sets of 3D Corresponding Point Pattern,
CAIP99(321-328).
Springer DOI 9909
BibRef

Rodrigues, M.A.[Marcos A.], Liu, Y.H.[Yong-Huai],
Distance Constraint Based Iterative Structure and Pose Estimation from a Single Image,
ICIP00(Vol I: 501-504).
IEEE DOI 0008
BibRef
Earlier:
Motion Parameter Constraints Analysis From a Single Image,
ICIP99(III:704-708).
IEEE DOI BibRef

David, P.[Philip], DeMenthon, D.F.[Daniel F.], Duraiswami, R.[Ramani], Samet, H.[Hanan],
SoftPOSIT: Simultaneous Pose and Correspondence Determination,
IJCV(59), No. 3, September-October 2004, pp. 259-284.
DOI Link 0405
BibRef
Earlier: ECCV02(III: 698 ff.).
Springer DOI 0205
BibRef
And:
Simultaneous pose and correspondence determination using line features,
CVPR03(II: 424-431).
IEEE DOI 0307
Combine Gold SoftAssign( See also New Algorithms for 2D and 3D Point Matching: Pose Estimation and Correspondence. ) and DeMenthon POSIT ( See also Iterative Pose Estimation Using Coplanar Feature Points. ). BibRef

David, P.[Philip], DeMenthon, D.F.[Daniel F.], Duraiswami, R.[Ramani], Samet, H.[Hanan],
Evaluation of the Softposit Model-to-image Registration Algorithm,
UMD-- TR4340, July 2002.
WWW Link. BibRef 0207

Kanatani, K.[Kenichi], Kanazawa, Y.S.[Yasu-Shi],
Automatic Thresholding For Correspondence Detection,
IJIG(4), No. 1, January 2004, pp. 21-33. 0401
BibRef

Lin, L.H.[Li-Heng], Lawrence, P.D.[Peter D.], Hall, R.[Robert],
Robust outdoor stereo vision SLAM for heavy machine rotation sensing,
MVA(24), No. 1, January 2013, pp. 205-226.
WWW Link. 1301
Camera pose using sun angle and shadows. Measure mining rope rotation about vertical axix. BibRef

Collins, T.[Toby], Bartoli, A.E.[Adrien E.],
Infinitesimal Plane-Based Pose Estimation,
IJCV(109), No. 3, September 2014, pp. 252-286.
Springer DOI 1408
Pose of a plane given set of corresponding points. BibRef

Zhou, H.Y.[Hao-Yin], Zhang, T.[Tao], Lu, W.N.[Wei-Ning],
Vision-Based Pose Estimation From Points With Unknown Correspondences,
IP(23), No. 8, August 2014, pp. 3468-3477.
IEEE DOI 1408
computer vision BibRef

Bratanic, B.[Blaž], Pernuš, F.[Franjo], Likar, B.[Boštjan], Tomaževic, D.[Dejan],
Real-time pose estimation of rigid objects in heavily cluttered environments,
CVIU(141), No. 1, 2015, pp. 38-51.
Elsevier DOI 1512
object pose estimation BibRef

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

Hossein-Nejad, Z.[Zahra], Nasri, M.[Mehdi],
RKEM: Redundant Keypoint Elimination Method in Image Registration,
IET-IPR(11), No. 5, April 2017, pp. 273-284.
DOI Link 1706
BibRef


Mahendran, S.[Siddharth], Ali, H.[Haider], Vidal, R.[René],
3D Pose Regression Using Convolutional Neural Networks,
DeepLearnRV17(494-495)
IEEE DOI 1709
Azimuth, Cameras, Solid modeling. Training. BibRef

Lourakis, M.[Manolis],
An efficient solution to absolute orientation,
ICPR16(3816-3819)
IEEE DOI 1705
Cameras, Covariance matrices, Estimation, Position measurement, Robots, Three-dimensional displays, Transmission, line, matrix, methods BibRef

Campbell, J.[Jordan], Mills, S.[Steven], Paulin, M.[Mike],
Mutual information of image intensity and gradient flux for markerless pose estimation,
ICVNZ15(1-6)
IEEE DOI 1701
particle swarm optimisation. Pose of articulated object. BibRef

Poirson, P.[Patrick], Ammirato, P.[Phil], Fu, C.Y.[Cheng-Yang], Liu, W.[Wei], Kosecka, J.[Jana], Berg, A.C.[Alexander C.],
Fast Single Shot Detection and Pose Estimation,
3DV16(676-684)
IEEE DOI 1701
Computational modeling BibRef

Tseng, H.Y.[Hung-Yu], Wu, P.C.[Po-Chen], Yang, M.H.[Ming-Hsuan], Chien, S.Y.[Shao-Yi],
Direct 3D pose estimation of a planar target,
WACV16(1-9)
IEEE DOI 1606
Cameras. 3D pose from 2D. First a template match, then refinement. BibRef

Jaspers, H., Mueller, G.R., Wuensche, H.J.[Hans-Joachim],
High accuracy model-based object pose estimation for autonomous recharging applications,
WACV16(1-7)
IEEE DOI 1606
Cameras BibRef

Tulsiani, S.[Shubham], Malik, J.[Jitendra],
Viewpoints and keypoints,
CVPR15(1510-1519)
IEEE DOI 1510
BibRef

Iversen, T.M.[Thorbjørn Mosekjær], Buch, A.G.[Anders Glent], Krüger, N.[Norbert], Kraft, D.[Dirk],
Shape Dependency of ICP Pose Uncertainties in the Context of Pose Estimation Systems,
CVS15(303-315).
Springer DOI 1507
BibRef

Bratanic, B.[Blaz], Likar, B.[Bostjan], Pernus, F.[Franjo], Tomazevic, D.[Dejan],
Pose estimation of textureless objects in cluttered environments,
MVA15(134-137)
IEEE DOI 1507
Computer vision 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

Tron, R.[Roberto], Daniilidis, K.[Kostas],
Statistical Pose Averaging with Non-isotropic and Incomplete Relative Measurements,
ECCV14(V: 804-819).
Springer DOI 1408
BibRef

Kneip, L.[Laurent], Li, H.D.[Hong-Dong], Seo, Y.D.[Yong-Duek],
UPnP: An Optimal O(n) Solution to the Absolute Pose Problem with Universal Applicability,
ECCV14(I: 127-142).
Springer DOI 1408
BibRef

Sweeney, C.[Chris], Flynn, J.[John], Turk, M.[Matthew],
Solving for Relative Pose with a Partially Known Rotation is a Quadratic Eigenvalue Problem,
3DV14(483-490)
IEEE DOI 1503
Cameras BibRef

Sweeney, C.[Chris], Fragoso, V.[Victor], Höllerer, T.[Tobias], Turk, M.[Matthew],
Large Scale SfM with the Distributed Camera Model,
3DV16(230-238)
IEEE DOI 1701
BibRef
Earlier:
gDLS: A Scalable Solution to the Generalized Pose and Scale Problem,
ECCV14(IV: 16-31).
Springer DOI 1408
BibRef

Kobayashi, T., Kato, H., Yanagihara, H.,
Novel Keypoint Registration for Fast and Robust Pose Detection on Mobile Phones,
ACPR13(266-271)
IEEE DOI 1408
augmented reality BibRef

Xiao, Y.[Yi], Lu, H.C.[Hu-Chuan], Li, S.F.[Shi-Feng],
Posterior constraints for double-counting problem in clustered pose estimation,
ICIP12(5-8).
IEEE DOI 1302
BibRef

Dondera, R.[Radu], Davis, L.S.[Larry S.],
Kernel PLS regression for robust monocular pose estimation,
MLVMA11(24-30).
IEEE DOI 1106
Evaluate five regression techniques for monocular 3D pose estimation. BibRef

Xu, W.[Wei], Mulligan, J.[Jane],
Robust relative pose estimation with integrated cheirality constraint,
ICPR08(1-4).
IEEE DOI 0812
RANSAC based pose BibRef

Alhwarin, F.[Faraj], Ristic-Durrant, D.[Danijela], Gräser, A.[Axel],
VF-SIFT: Very Fast SIFT Feature Matching,
DAGM10(222-231).
Springer DOI 1009
BibRef

Vuppala, S.K.[Sai Krishna], Gräser, A.[Axel],
An Approach for Tracking the 3D Object Pose Using Two Object Points,
CVS08(xx-yy).
Springer DOI 0805
BibRef

Yang, M.[Ming], Yu, Q.[Qian], Wang, H.[Hong], Zhang, B.[Bo],
Vision based real-time pose estimation for intelligent vehicles,
IVS04(262-267).
WWW Link. 0411
Ground plane assumption. Gradient Angle Histogram. BibRef

Yang, M.[Ming], Dong, B.[Bin], Wang, H.[Hong], Zhang, B.[Bo],
Real-time pose estimation for outdoor, mobile robots using range data,
ICPR02(II: 593-596).
IEEE DOI 0211
BibRef

Ude, A.[Ales],
Nonlinear Least Squares Optimisation of Unit Quaternion Functions for Pose Estimation from Corresponding Features,
ICPR98(Vol I: 425-427).
IEEE DOI 9808
BibRef

Jacobs, D.W.,
Optimal Matching of Planar Models in 3D Scenes,
CVPR91(269-274).
IEEE DOI Point features on a flat object to point features in 3-D in an arbitrary pose. Newer: oriented points require more space in the indexing scheme. BibRef 9100

Hel-Or, Y., and Werman, M.,
Absolute Orientation from Uncertain Point Data: A Unified Approach,
CVPR92(77-82).
IEEE DOI Pose using a model, predicted 3-D from 2-D BibRef 9200

Wang, Z., Jepson, A.D.,
A New Closed-Form Solution for Absolute Orientation,
CVPR94(129-134).
IEEE DOI BibRef 9400

Lu, L.[Liu], Luo, F.[Fang], Mulder, N.J.,
Recognition of 2-D Objects by Optimal Matching,
BMVC94(xx-yy).
PDF File. 9409
BibRef

Chapter on Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces continues in
Point Pattern Invariants .


Last update:Nov 11, 2017 at 13:31:57