Simultaneous line matching and epipolar geometry estimation based on the intersection context of coplanar line pairs

This paper presents a novel line matching method based on the intersection context of coplanar line pairs. The proposed method is designed to be especially effective for dealing with poorly structured and/or textured scenes. To overcome the ambiguity in line matching based on single line segments, the intersecting line pairs in 2D images that are coplanar in 3D are chosen instead for use in matching. The coplanarity of intersecting line pairs and their corresponding intersection context discriminate the true intersecting line pairs from the false intersecting ones in 3D. Compared to previous approaches, the method proposed herein offers efficient yet robust matching performance under poor line topologies or junction structures, while simultaneously estimating unknown camera geometry. This is due to the fact that the proposed method neither resorts to comprehensive topological relations among line segments nor relies on the presence of well-defined junction structures. The intersecting line pairs, used here as matching features, are more informative than the single line segments and simpler than the comprehensive topological relations. Also, the coplanarity criteria are more generally applied than the requirement of junction structures. Comparison studies and experimental results prove the accuracy and speed of the proposed method for various real world applications.

[1]  Adrien Bartoli,et al.  Multiple-view structure and motion from line correspondences , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[2]  Philip H. S. Torr,et al.  The Development and Comparison of Robust Methods for Estimating the Fundamental Matrix , 1997, International Journal of Computer Vision.

[3]  Jan-Michael Frahm,et al.  Towards Urban 3D Reconstruction from Video , 2006, Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06).

[4]  Cordelia Schmid,et al.  Evaluation of Interest Point Detectors , 2000, International Journal of Computer Vision.

[5]  R. Hartley Triangulation, Computer Vision and Image Understanding , 1997 .

[6]  Martial Hebert,et al.  Preliminary Development of a Line Feature-Based Object Recognition System for Textureless Indoor Objects , 2007 .

[7]  Sukhan Lee,et al.  Planar Patch based 3D Environment Modeling with Stereo Camera , 2007, RO-MAN 2007 - The 16th IEEE International Symposium on Robot and Human Interactive Communication.

[8]  Zhanyi Hu,et al.  MSLD: A robust descriptor for line matching , 2009, Pattern Recognit..

[9]  Cordelia Schmid,et al.  A Comparison of Affine Region Detectors , 2005, International Journal of Computer Vision.

[10]  Ian D. Reid,et al.  Single View Metrology , 2000, International Journal of Computer Vision.

[11]  Reinhard Koch,et al.  Self-Calibration and Metric Reconstruction Inspite of Varying and Unknown Intrinsic Camera Parameters , 1999, International Journal of Computer Vision.

[12]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[13]  Luc Van Gool,et al.  3D from Line Segments in Two Poorly-Textured, Uncalibrated Images , 2006, Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06).

[14]  Luc Van Gool,et al.  Wide-baseline stereo matching with line segments , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

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

[16]  Steven M. Seitz,et al.  Photo tourism: exploring photo collections in 3D , 2006, ACM Trans. Graph..

[17]  Jana Kosecka,et al.  Piecewise planar city 3D modeling from street view panoramic sequences , 2009, CVPR.

[18]  Yeung Sam Hung,et al.  Projective reconstruction from line-correspondences in multiple uncalibrated images , 2006, Pattern Recognit..

[19]  Wen Gao,et al.  Image Matching by Multiscale Oriented Corner Correlation , 2006, ACCV.

[20]  Cordelia Schmid,et al.  AUTOMATIC LINE MATCHING AND 3D RECONSTRUCTION OF BUILDINGS FROM MULTIPLE VIEWS , 1999 .

[21]  Richard I. Hartley,et al.  In Defense of the Eight-Point Algorithm , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Lu Wang,et al.  Wide-baseline image matching using Line Signatures , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[23]  Jiri Matas,et al.  Robust wide-baseline stereo from maximally stable extremal regions , 2004, Image Vis. Comput..

[24]  Andrew Zisserman,et al.  New Techniques for Automated Architectural Reconstruction from Photographs , 2002, ECCV.

[25]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[26]  Robert Laganière,et al.  Junction Matching and Fundamental Matrix Recovery in Widely Separated Views , 2004, BMVC.