Direct Method for Motion Estimation: An Alternative to Decomposition of Planar Transformation Matrices

Recently, we have seen a proliferation in research addressing the motion estimation problem and its practical applications based on coplanar point configurations [10,14,12,9,6,7,13]. This paper introduces a new approach where following an estimation of the full projection matrix from non-coplanar points in one reference frame, the system provides better motion estimation results based on coplanar point configurations without estimating the camera intrinsic parameters. The new mathematical framework allows us to directly estimate the rigid transformation between a full projection matrix and a homography. Experimental results compare the accuracy of this and the homography decomposition approach, proposed in [14], in the context of an augmented reality application where three cameras are calibrated for real-time image augmentation.

[1]  Olivier Faugeras,et al.  Motion and Structure from Motion in a piecewise Planar Environment , 1988, Int. J. Pattern Recognit. Artif. Intell..

[2]  Olivier Faugeras,et al.  Three-Dimensional Computer Vision , 1993 .

[3]  Sundaram Ganapathy,et al.  Decomposition of transformation matrices for robot vision , 1984, Pattern Recognit. Lett..

[4]  O. Faugeras.  Three-dimensional computer vision: a geometric viewpoint , 1993 .

[5]  N. Navab,et al.  Tracking and pose estimation for computer assisted localization in industrial environments , 2000, Proceedings Fifth IEEE Workshop on Applications of Computer Vision.

[6]  Andrew W. Fitzgibbon,et al.  Markerless tracking using planar structures in the scene , 2000, Proceedings IEEE and ACM International Symposium on Augmented Reality (ISAR 2000).

[7]  Hirokazu Kato,et al.  Marker tracking and HMD calibration for a video-based augmented reality conferencing system , 1999, Proceedings 2nd IEEE and ACM International Workshop on Augmented Reality (IWAR'99).

[8]  Peter F. Sturm,et al.  Algorithms for plane-based pose estimation , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[9]  Zhengyou Zhang,et al.  A Flexible New Technique for Camera Calibration , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Amnon Shashua,et al.  On the synthesis of dynamic scenes from reference views , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[11]  Roger Y. Tsai,et al.  A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses , 1987, IEEE J. Robotics Autom..

[12]  Yakup Genc,et al.  Augmented workspace: designing an AR testbed , 2000, Proceedings IEEE and ACM International Symposium on Augmented Reality (ISAR 2000).

[13]  Jun Rekimoto,et al.  CyberCode: designing augmented reality environments with visual tags , 2000, DARE '00.