This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Determining Pose of 3D Objects With Curved Surfaces
January 1996 (vol. 18 no. 1)
pp. 52-57

Abstract—A method is presented for computing the pose of rigid 3D objects with arbitrary curved surfaces. Given an input image and a candidate object model and aspect, the method will verify whether or not the object is present and if so, report pose parameters. The curvature method of Basri and Ullman is used to model points on the object rim, while stereo matching is used for internal edge points. The model allows an object edgemap to be predicted from pose parameters. Pose is computed via an iterative search for the best pose parameters. Heuristics are used so that matching can succeed in the presence of occlusion and artifact and without resorting to use of corresponding salient feature points. Bench tests and simulations show that the method almost always converges to ground truth pose parameters for a variety of objects and for a broad set of starting parameters in the same aspect.

[1] R. Bajcsy and F. Solina, "Three-dimensional object representation revisited," Proc. First Int'l. Conf. Computer Vision, pp. 231-240, 1987.
[2] R. Basri and S. Ullman, "The alignment of objects with smooth surfaces," Proc. Second Int'l Conf. Computer Vision, pp. 482-488, 1988.
[3] K. Bowyer, D. Eggert, J. Stewman, and L. Stark, "Developing the aspect graph representation for use in image understanding," Proc. DARPA Image Understanding Workshop,Palo Alto, pp. 831-849, 1989.
[4] J.-L. Chen, G. Stockman, and K. Rao, "Recovering and tracking pose of curved 3D objects from 2D images," Proc. IEEE Conf. Computer Vision and Pattern Recognition,New York, N.Y., pp. 233-239, June 1993.
[5] C. Goad, "Special purpose automatic programming for 3D model-based vision," Proc. DARPA Image Understanding Workshop,Arlington, VA, 1983.
[6] A.D. Gross and T.E. Boult,“Error of fit for recovering parametric solids,” Second Int’l Conf. Computer Vision, pp. 690-694,Tampa, Fla., 1988.
[7] A. Gupta,“Surface and volumetric segmentation of complex 3D objects using parametric shape models,” PhD thesis, Dept. of Computer and Information Science, Univ. of Pennsylvania, 1991.
[8] K. Higuchi, H. Delingette, M. Hebert, and K. Ikeuchi, "Merging multiple views using a spherical representation," Proc. IEEE Second CAD-Based Vision Workshop,Champion, Penn., pp. 124-131, 1994.
[9] D. Keren,D. Cooper,, and J. Subrahmonia,“Describing complicated objects by implicit polynomials,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 16, no. 1, pp. 38-53, 1994.
[10] J.J. Koenderink and A.J. van Doorn, "Internal representation of solid shape with respect to vision," Biological Cybernetics, vol. 32 no. 4, pp. 211-216, 1979.
[11] D.J. Kriegman and J. Ponce, "On Recognizing and Positioning Curve 3-D Objects From Image Contours," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 12, pp. 1,127-1,137, Dec. 1990.
[12] D.G. Lowe, “Three-Dimensional Object Recognition from Single Two-Dimensional Images,” Artificial Intelligence, vol. 31, pp. 355-395, 1987.
[13] D.G. Lowe, "Fitting Parameterized Three-Dimensional Models to Images," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 13, no. 5, pp. 441-450, May 1991.
[14] A. Pentland, B. Moghaddam, and Starner, "View-Based and Modular Eigenspaces for Face Recognition," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 1994, pp. 84-91.
[15] J. Ponce, D. Forsyth, L. Shapiro, R. Bajcsy, D. Metaxas, M. Hebert, K. Ikeuchi, S. Sclaroff, A. Pentland, T. Binford, A. Kak, and G. Stockman, "Object representation for object recognition," Proc. IEEE Conf. Computer Vision and Pattern Recognition,Seattle, Wash., pp. 147-152, June 1994.
[16] G. Taubin,“Estimation of planar curves, surfaces, and nonplanar space curves defined by implicit equations with applications to edge and range image segmentation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 13, no. 11, pp. 1115-1137, Nov. 1991.
[17] S. Ullman,“Aligning pictorial descriptions: An approach to object recognition,” Cognition, vol. 32, pp. 193-254, 1989. Also: in MIT AI Memo 931, Dec. 1986.

Index Terms:
Pose determination, 3D objects, object tracking, object modeling, image matching, recognition by alignment.
Citation:
Jin-Long Chen, George C. Stockman, "Determining Pose of 3D Objects With Curved Surfaces," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 1, pp. 52-57, Jan. 1996, doi:10.1109/34.476010
Usage of this product signifies your acceptance of the Terms of Use.