Efficient lookup table based camera pose estimation for augmented reality

Computer Animation and Virtual Worlds

Volumn 22, Issue 1, 2011, Pages 47-58

  Li, Shiqi ^a   Xu, Chi ^a  

^a HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

augmented reality; camera pose estimation; lookup table

Indexed keywords

AR APPLICATION; CAMERA POSE ESTIMATION; COMPUTATIONAL RESOURCES; COMPUTATIONAL SPEED; CORNER POINT; HIGH STABILITY; HIGHLY SENSITIVE; ITERATIVE SOLUTIONS; KEY PARAMETERS; LOOK UP TABLE; NON-ITERATIVE; POINT CORRESPONDENCE; SYMMETRICAL STRUCTURE;

AUGMENTED REALITY; CAMERAS; COMPUTATIONAL COMPLEXITY; ESTIMATION; VIRTUAL REALITY;

TABLE LOOKUP;

EID: 79952033376     PISSN: 15464261     EISSN: 1546427X     Source Type: Journal    
DOI: 10.1002/cav.385     Document Type: Article

References (33)

1. Kalkofen D, Mendez E, Schmalstieg D,. Interactive focus and context visualization for augmented reality. In Proceedings of the 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality, 2007; 1-10.
2. Wang X, Dunston P,. Compatibility issues in augmented reality systems for AEC: an experimental prototype study. Automation in Construction 2006; 15 (3): 314-326. (Pubitemid 43332715)
3. Fischer J, Eichler M, Bartz D, Straber W,. A hybrid tracking method for surgical augmented reality. Computers and Graphics 2007; 31: 39-52. (Pubitemid 46241414)
4. Zhu J, Pan Z, Sun C, Chen W,. Handling occlusions in video-based augmented reality using depth information. Computer Animation and Virtual Worlds. 2010; 21 (5): 509-521.
5. Jonietz E,. MIT Technology Review: Special Issue 10 Emerging Technologies, 2007.
6. Papagiannakis G, Singh G, Magnenat-Thalmann N,. A survey of mobile and wireless technologies for augmented reality systems. Computer Animation and Virtual Worlds 2008; 19 (1): 3-22.
7. Schweighofer G, Pinz A,. Robust pose estimation from a planar target. IEEE Transactions on Pattern Analysis and Machine Intelligence 2006; 28 (12): 2024-2030. (Pubitemid 46405712)
8. Yuan M, Ong S, Nee A,. A generalized registration method for augmented reality systems. Computers and Graphics 2005; 29 980-997. (Pubitemid 41660888)
9. Graz University. 2009. Handheld Augmented Reality Project. Available at http://studierstube.icg.tu-graz.ac.at/handheld-ar/
10. Klein G, Murray D,. Parallel tracking and mapping for small AR workspaces. In Proceedings of the 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality, IEEE Computer Society, 2007; 1-10.
11. Klein G, Murray D,. Parallel tracking and mapping on a camera phone. In Proceedings of the 2009 8th IEEE International Symposium on Mixed and Augmented Reality, IEEE Computer Society, 2009; 83-86.
12. ARToolKit. 2003. Artoolkit Home Page. Available at http://www.hitl. washington.edu/artoolkit/
13. ARTag 2009. Artag Home Page. Available at http://www.artag.net/
14. Claus D, Fitzgibbon A,. Reliable Fiducial Detection in Natural Scenes. In European Conference on Computer Vision (ECCV'04), 2004; 469.
15. Fiala M,. ARTag, a fiducial marker system using digital techniques. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), Vol. 2, 2005.
16. Zhang X, Fronz S, Navab N,. Visual marker detection and decoding in AR systems: a comparative study. In International Symposium on Mixed and Augmented Reality (ISMAR'02), 2002; 97-106.
17. Malik S, Roth G, Mcdonald C,. Robust 2D tracking for real-time augmented reality. In Proceedings of Vision Interface (VI), 2002.
18. Abidi M, Chandra T,. A new efficient and direct solution for pose estimation usingquadrangular targets: algorithm and evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence 1995; 17 (5): 534-538.
19. Bujnak M, Kukelova Z, Pajdla T,. A general solution to the P4P problem for camera with unknown focal length. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR'08), 2008; 1-8.
20. Ansar A, Daniilidis K,. Linear pose estimation from points or lines. IEEE Transactions on Pattern Analysis and Machine Intelligence 2003; 25 (5): 578-589.
21. Lepetit, V,., Moreno-Noguer, F, Fua, P, EPnP: an accurate O (n) solution to the PnP problem International Journal of Computer Vision 2009; 81 (2): 155-166.
22. Araujo H, Carceroni R, Brown C,. A fully projective formulation to improve the accuracy of Lowe's pose-estimation algorithm. Computer Vision and Image Understanding 1998; 70 (2): 227-238. (Pubitemid 128440426)
23. Lu C, Hager G, Mjolsness E,. Fast and globally convergent pose estimation from video images. IEEE Transactions on Pattern Analysis and Machine Intelligence 2000; 22 (6): 610-622.
24. Wagner D, Schmalstieg D,. Artoolkitplus for pose tracking on mobile devices. In Proceedings of 12th Computer Vision Winter Workshop (CVWW'07), 2007; 139-146.
25. Rohs M,. Marker-based embodied interaction for handheld augmented reality games. Journal of Virtual Reality and Broadcasting 2007; 4 (5).
26. Schall G, Mendez E, Kruijff E, Veas E, Junghanns S, Reitinger B, Schmalstieg D,. Handheld augmented reality for underground infrastructure visualization. Personal and Ubiquitous Computing 2009; 13 (4): 281-291.
27. Umeyama S,. Least-squares estimation of transformation parameters between twopoint patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 1991; 13 (4): 376-380. (Pubitemid 21645719)
28. Zhang Z,. A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence 2000; 22 (11): 1330-1334.
29. Graz University. Artoolkitplus Home Page. 2006. Available at http://studierstube.icg.tu-graz.ac.at/handheld-ar/artoolkitplus.php
30. Bay H, Ess A, Tuytelaars T, Van Gool L,. Speeded-up robust features (SURF). Computer Vision and Image Understanding 2008; 110 (3): 346-359.
31. Rosten E, Drummond T,. Machine learning for high-speed corner detection. Computer Vision-ECCV 2006, 2006; 430-443. (Pubitemid 44029797)
32. Taylor S, Rosten E, Drummond T,. Robust feature matching in 2.3 μ. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2009, 2009; 15-22.
33. Bouthemy P,. A maximum likelihood framework for determining moving edges. IEEE Transactions on Pattern Analysis and Machine Intelligence 1989; 11 (5): 499-511.