Omnidirectional Camera Calibration and 3D Reconstruction by Contour Matching

This paper presents a novel approach to both omnidirectional camera calibration and 3D reconstruction of the surrounding scene by contour matching in architectural scenes. By using a quantitative measure to consider the inlier distribution, we can estimate more precise camera model parameters and structure from motion. Since most of line segments of man-made objects are projected to the contours in omnidirectional images, contour matching problem is important in camera recovery process. We propose a novel 3D reconstruction method by contour matching in three omnidirectional views. First, two points on the contour and their viewing vectors are used to determine an interpretation plane equation, and we obtain a contour intersecting both the plane and the estimated patch of the camera model. Then, 3D line segment is calculated from two patches, which is projected to the contour on the third views, and these matching results are used in refinement of camera recovery.

[1]  Kenneth Turkowski,et al.  Creating image-based VR using a self-calibrating fisheye lens , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[2]  Ben J. A. Kröse,et al.  Robust scene reconstruction from an omnidirectional vision system , 2003, IEEE Trans. Robotics Autom..

[3]  Roman Kuc Forward model for sonar maps produced with the Polaroid ranging module , 2003, IEEE Trans. Robotics Autom..

[4]  Tomás Pajdla,et al.  Estimation of omnidirectional camera model from epipolar geometry , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[5]  David J. Kriegman,et al.  Structure and Motion from Line Segments in Multiple Images , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Tomás Pajdla,et al.  Omnidirectional Camera Model and Epipolar Geometry Estimation by RANSAC with Bucketing , 2003, SCIA.

[7]  Robin N. Strickland,et al.  Contour motion estimation using relaxation matching with a smoothness constraint on the velocity field , 1994 .

[8]  Tomas Pajdla,et al.  3D Metric Reconstruction from Uncalibrated Omnidirectional Images , 2004 .

[9]  Joon Hee Han,et al.  Contour Matching Using Epipolar Geometry , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Cordelia Schmid,et al.  Automatic line matching across views , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[11]  James J. Kumler,et al.  Fish-eye lens designs and their relative performance , 2000, SPIE Optics + Photonics.

[12]  Frank A. van den Heuvel,et al.  Line-photogrammetric mathematical model for the reconstruction of polyhedral objects , 1998 .

[13]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Katsushi Ikeuchi,et al.  Acquiring a Radiance Distribution to Superimpose Virtual Objects onto Real Scene , 2001, MVA.