Abstract
This paper aims to propose a novel and simple method for estimating 3D-point reconstruction and camera motion. Given a video sequence of a target object with a few feature-points tracked, the points’ 3D-coordinates can be reconstructed along with the estimation of the camera’s position and orientation in each frame. The proposed method is based on combining Powell’s method using parabolic graph with the well-known Gradient Descent to guess the direction to estimate the unknown variables. The unknowns include six components for camera pose in each frame, one focal length, and three values for each point. Using this proposed method, the problem of missing points due to selfocclusion can be eliminated without using any other special strategies. A synthetic experiment shows accuracy of computing 3D-points and the camera pose in each frame. A real-world experiment from only one off-the-shelf digital camera is also shown to demonstrate the robustness of our approach.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn., Cambridge (2006)
Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical Recipes: The art of Scientific Computing, 3rd edn., Cambridge (2007)
Karayiannis, N.B.: Reformulated Radial Basis Neural Networks Trained by Gradient Descent. IEEE Transactions on Neural Networks 10(3), 657–671 (2000)
Chen, O.T.-C.: Motion Estimation Using a One-Dimensional Gradient Descent Search. IEEE Transactions on Circuits and Systems for Video Technology 10(4), 608–616 (2000)
Guerrero, J.J., Sagues, C.: Estimating the Motion Direction from Brightness Gradient on Lines. IEEE Transactions on Systems, Man, and Cybernetics – Part C: Applications and Reviews 31(3), 419–426 (2001)
Po, L.M., Ng, K.H., Cheung, K.W., Wong, K.M., Uddin, Y., Ting, C.W.: Novel Directional Gradient Descent Searches for Fast Block Motion Estimation. IEEE Transactions on Circuits and Systems for Video Technology 19(8), 1189–1195 (2009)
Smolic, A.: Robust Generation of 360-Degree Panoramic Views from Consumer Video Sequences. In: 4th EURASIP-IEEE Region 8 International Symposium on Video/Image Processing and Multimedia Communications (VIPromCom), June 16-19, pp. 431–435 (2002)
Edwards, G.J., Taylor, C.J., Cootes, T.F.: Interpreting Face Images using Active Appearance Models. In: 3rd IEEE International Conference on Automatic Face and Gesture Recognition, April 14-16, pp. 300–305 (1998)
Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active Appearance Models. In: Burkhardt, H., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1407, pp. 484–498. Springer, Heidelberg (1998)
Chouvatut, V., Madarasmi, S.: A Comparison of Two Camera Pose Methods for Augmented Reality. In: 7th IASTED International Conference on Signal and Image Processing (SIP), August 15-17, pp. 554–559 (2005)
Chouvatut, V., Madarasmi, S.: Estimation of Camera Pose for Use in Augmented Reality System. In: 20th International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC), July 4-7, vol. 3, pp. 979–980 (2005)
Tsai, R.Y.: A Versatile Camera Calibration Technique for High-Accuracy 3D Machine Vision Metrology Using Off-the-Shelf TV Camera and Lenses. IEEE Journal of Robotics and Automation RA-3(4), 323–344 (1987)
Kato, H., Billinghurst, M.: Marker Tracking and HMD Calibration for a Video-Based Augmented Reality Conferencing System. In: Proceeding 2nd IEEE and ACM International Workshop on Augmented Reality, October 1999, pp. 85–94 (1999)
Okuma, T., Sakaue, K., Takemura, H., Yokoya, N.: Real-Time Camera Parameter Estimation from images for a Mixed Reality System. In: IEEE Proceeding 15th International Conference on Pattern Recognition, September 3-7, vol. 4, pp. 482–486 (2000)
Hartley, R.I.: Projective Reconstruction and Invariants from Multiple Images. IEEE Transactions on Pattern Analysis and Machine Intelligence 16(10), 1036–1041 (1994)
Avidan, S., Shashua, A.: Novel View Synthesis by Cascading Trilinear Tensors. IEEE Transactions on Visualization and Computer Graphics 4(4), 293–306 (1998)
Li, J., Chellappa, R.: A Factorization Method for Structure from Planar Motion. In: IEEE Workshop on Motion and Video Computing (WACV/MOTIONS), January 2005, vol. 2, pp. 154–159 (2005)
Sasson, A.M.: Combined Use of the Powell and Fletcher – Powell Nonlinear Programming Methods for Optimal Load Flows. IEEE Transactions on Power Apparatus and Systems PAS-88(10), 1530–1537 (1969)
Xu, X., Dony, R.D.: Differential Evolution with Powell’s Direction Set Method in Medical Image Registration. In: IEEE International Symposium on Biomedical Imaging: Nano to Micro, April 15-18, vol. 1, pp. 732–735 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chouvatut, V., Madarasmi, S., Tuceryan, M. (2010). 3D Reconstruction and Camera Pose from Video Sequence Using Multi-dimensional Descent. In: Prasad, S.K., Vin, H.M., Sahni, S., Jaiswal, M.P., Thipakorn, B. (eds) Information Systems, Technology and Management. ICISTM 2010. Communications in Computer and Information Science, vol 54. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12035-0_28
Download citation
DOI: https://doi.org/10.1007/978-3-642-12035-0_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12034-3
Online ISBN: 978-3-642-12035-0
eBook Packages: Computer ScienceComputer Science (R0)